phd thesis on iris recognition

A Proposed Biometric Technique for Improving Iris Recognition

• Research Article
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• Published: 16 September 2022
• Volume 15 , article number  79 , ( 2022 )

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• Rahmatallah Hossam Farouk   ORCID: orcid.org/0000-0001-7404-6448 1 ,
• Heba Mohsen 1 &
• Yasser M. Abd El-Latif 2 , 3  

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Recently, the Iris Recognition system has been considered an effective biometric model for recognizing humans. This paper introduces an effective hybrid technique combining edge detection and segmentation, in addition to the convolutional neural network (CNN) and Hamming Distance (HD), for extracting features and classification. The proposed model is applied to different datasets, which are CASIA-Iris-Interval V4, IITD, and MMU. For validating the results of the proposed models, detailed modeling and simulation procedures took place using the mentioned three datasets. A comparison between the obtained results from the current work and published results from open literature was carried out as well. The Proposed Biometric Technique showed desirable recognition accuracies of 94.88% based on applying HD on CASIA, 96.56% based on applying CNN on IITD, and 98.01% based on applying CNN on MMU. The obtained accuracies illustrated the superiority of such a classifier compared to other classifiers used in the published literature.

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1 Introduction

The growing desire for increased security in everyday life because of digitalization has prompted the development of a reliable and intelligent biometric-based person identification system. The measuring and statistical analysis of people's unique physical and behavioral features is known as biometrics. The technology is mostly used for identification and access control. Traditional identifying methods make use of cards or passwords. These methods can be harmed by misplacing or stealing cards, as well as forgetting passwords. That is why biometric identification technologies that can identify people without relying on what they have or what they recall are much required.

The scientific community tested several biometric approaches for human recognition, such as the fingerprint, voice, face, iris, signature, retina, palm print, gait, and hand geometry [ 1 ]. These characteristics are more trustworthy (although they do have certain limitations) compared to the other traditional security systems. For example, speech biometric security apps suffer some problems such as when various persons have identical voices, the effect of both health and aging, and how the media is transmitted. In addition, the signature biometric apps have their own set of constraints. For example, given practice, it is possible to copy someone's signature. On the other hand, face biometrics are influenced by non-uniform lighting, beards, scars, age indications, and wounds. Fingerprint biometrics are also subject to finger contamination with oil, grease, and other substances. The retina biometric has limitations as well since it can be damaged if overexposed to infrared illumination.

Iris biometric systems have garnered increased attention in recent decades because of their distinctiveness and relevance as a biometric authentication method. Using pattern recognition and digital image processing techniques, this system distinguishes persons based on the texture of their iris. Figure  1 shows that the iris is an annulus that sits between the eye pupil and the sclera. It is an externally visible internal organ that is protected by the cornea. According to the literature, a human's iris remains steady throughout his or her life, except for a few minor modifications throughout childhood [ 2 ].

Eye anatomy

The proposed Iris Recognition system consists of the following steps: edge detection, iris segmentation, feature extraction and classification. In this paper, edge detection is done using Canny Edge Detection technique, segmentation is achieved using Hough transform for localizing the iris and pupil regions, and then CNN and HD used for feature extraction and classification to increase the accuracy of the Iris Recognition.

The rest of the paper is arranged as follows: Sect. 2 displays the related work utilized in developing the suggested models. Section 3 explains the phases of the proposed system in detail. Firstly, in Sect. 3.1, the three used datasets are described in detail. After that, sub-Sects. 3.2 and 3.3 describes the edge detection and segmentation hybrid technique applied on the image on one of the datasets; Casia-Iris-Interval, IITD or MMU. At the end of Sect. 3, feature extraction and classification using CNN and HD are mentioned. Section 4 displays the experimental results and discussion. Finally, Sect. 5 provides a summary of the entire study.

2 Related Work

In this section, the authors introduced a comprehensive survey of related research that revealed some modern analysis studies of Iris Recognition. The following related work are various Iris Recognition systems discussed as a whole system with different classifiers. Our proposed system is used some of the datasets and classifiers mentioned the related work.

Alaslani et al. [ 3 ] introduced the evaluation of the extracted learned features from a pre-trained convolutional neural network (CNN) (Alex-Net) followed by a multi-class support vector machine (SVM) algorithm to implement Iris Recognition. Support Vector Machine is generally thought to be a classification technique; however, they may be used in both classification and regression issues. It can handle a large number of continuous and categorical variables with ease. To differentiate various classes, SVM creates a hyperplane in multidimensional space. SVM iteratively develops ideal hyperplanes, which are used to minimize errors. The circular Hough transform (HT) was used to perform the iris segmentation, while the rubber sheet model was used for the normalization. The resulting image was fed as an input to the CNN. The proposed model experimented on public datasets, such as CASIA-Iris-V1, IITD iris databases, CASIA-Iris-Interval, and CASIA-Iris-thousand. The achieved results showed that the accuracy of the presented system is higher than when extracting characteristics from the normalized photos.

Yiming et al. [ 4 ] presented a HT-based Iris Recognition method. The iris image was first pre-processed, and then the iris boundary was discovered using the HT in conjunction with the Canny edge detection. The annular iris region was then normalized into a rectangular area using the Rubber sheet method, and the 1D Log-Gabor filter pair was applied. The textural properties of the normalized iris picture were retrieved, and the final pattern matching was done using a recognition method based on the Hamming Distance (HD) classifier. The CASIA V1.0 iris image database was utilized as the experimental object in this study, and the simulation was implemented on the MATLAB IDE to evaluate the effectiveness of the Iris Recognition algorithm. The demonstrated experiments proved that Iris Recognition is an applicable and effective technology.

The Radon Transform Thresholding (RTT) and Gradient-based Isolation (GI) approaches were proposed in [ 5 ]. The significant characteristics of the pre-processed picture were extracted using RTT. GI is a pre-processing approach that takes advantage of the Gradient operator's edge detecting capability and isolates the patterns to get the most important iris textures. A feature based on Binary Particle Swarm Optimization (BPSO) was used. The feature vector space was searched using a selection technique. The experiments were done on three different databases: Phoenix, IITD, and CASIA. The feature extractor's performance was evaluated for a variety of block sizes. The total number of blocks in the image grew as the block size decreased, increasing the calculation time per image. The average testing time for the Phoenix database was 700 ms per image on a PC with an Intel Core i7, 2.4 GHz CPU, and 8 GB RAM, which was a constraint for real-time applications.

Liu et al. [ 6 ] utilized Gaussian to pre-process images by fuzzifying the area outside the border, using triangle fuzzy average and triangular fuzzy median smoothing filters. They used the upgraded photos to train deep learning (DL) systems using fuzzy operations, which sped up the convergence process and improved identification accuracy. Fuzzified image filters made pictures more instructive for DL, as seen by the saliency maps. Many more deep learning applications of image processing, analysis, and prediction may benefit from the suggested fuzzy image operation.

Danlami et al. [ 7 ] evaluated the influence of the Legendre Wavelet filter through the Gabor Wavelet filter in an experiment. They used the databases CASIA V4 distances and intervals, UBIRIS V2, and MMU V2. They looked at the complete database, except for the partially captured image and the angled photos. The researchers used the HT, Rubber sheet, and HD methods for performing the segmentation, normalization, and matching, respectively.

Data Augmentation is a well-known approach in image processing, particularly in computer vision, for increasing the diversity and quantity of training data by performing random (but realistic) modifications. Image resizing, rotation, and flipping are just a few examples. This strategy allows us to obtain a more diversified nature of previously contained data, resulting in a better training set and, as a result, a better-trained model. The data augmentation has aided in the generation of enormous datasets on individuals, which has made developing improved deep learning models for iris identification challenging. Deep convolutional networks and a mixed convolutional network were suggested in [ 8 ]. The ADAM optimizer, which generates gradients using adaptive momentum, had a better learning method and process than the Stochastic Gradient Descent with Momentum (SGDM). The hybrid CNN with SVM, on the other hand, outperformed the raw CNN architecture in terms of accuracy. This was due to the SVM's capacity to deal with features in multidimensional space. It also avoided the use of handmade segmentation, which is used in numerous deep learning algorithms, while still delivering comparable results. This study was enhanced by looking into other learning optimizers and adding new layers. Also, the other hyper-parameter adjustments can be investigated. It may present an opportunity for the classifier's performance to be improved further. Due to the multi-dimensional elasticity of the SVM rather than the fully connected layer, the hybrid CNN with SVM performs better than the traditional CNN. However, there are a few gaps in this strategy. The suggested methodologies' performance measurements are confined to the IITD database, and the network's performance may fail for other iris datasets. The other problem is that the hybrid structure requires too many calculations. Even if convolutional features are more distinguishable, deep structures and huge data samples need more computation.

A sparse representation of the iris identification model based on compressive sensing and k-nearest segments was presented in Bhateja et al. [ 9 ]. The k-nearest subspace technique was utilized for shortlisting the classes to minimize time. The selected applicants were separated into sectors, and each field was given scant respect. Three classifiers were used: the k-nearest distance classifier, the sector-based classifier, and the Cumulative Sparse Concentration Index (CSCI)-based classifier. A classifier combination technique based on additive functions was used, with each classifier having its weight which is learned using a Genetic Algorithm. The technique is very resilient, with a FAR of practical nil, according to results acquired from several datasets.

Nishanth et al. [ 10 ] presented a unique Iris Recognition system that enhanced the recognition rate and minimized the number of features evaluated by conducting feature extraction using the Gabor filter and Discrete Cosine Transform, followed by feature selection using Dynamic Binary Particle Swarm Optimization (DBPSO). Binary Particle Swarm Optimization (BPSO) is a discrete form of PSO that changes particle velocities depending on the likelihood that a particle co-ordinate will change to either 0 or 1. The number of features picked was greatly reduced when the number of iterations of the proposed DBPSO algorithm was increased, with only a little trade-off in the average recognition rate. The relevance of this strategy was that it is dataset-independent and assures that the number of features picked was reduced regardless of the dataset. In the IITD dataset, the suggested technique achieved an average recognition rate of 96.46%, while in the MMU dataset, it achieved a rate of 78.07%.

Tallapragada et al. [ 11 ] presented a novel segmentation technique for segmenting the partially visible iris region in this work. The suggested segmentation achieved 90% accuracy throughout the MMU iris dataset and took 1.8 s to segment each iris. Different features were taken and integrated from the segmented iris region to generate a feature vector. They were classified using the decision tree classifier, which is made up of a rooted tree that is driven by a node called root, which is prime, and the rest of the nodes are called leaves. The algorithm of decision trees automatically generated a decision tree for the provided dataset such that the inaccuracy was as little as possible. The decision tree classifier attempted to find a decision tree T with a given cost function and optimize the cost function. L-labeled collection samples were attempted to optimize the decision. When a tree was supplied, it would search for the best class from the given dataset.

Innovative strategies were suggested in [ 12 ], that is, Contrast Enhancement employment, Top Hat and Bottom Hat filters to improve the gradience between brighter and darker pixels, and DWT + DCT feature extractor to identify the important features. A feature selection approach based on BPSO was utilized to explore the feature space for the optimum feature subset. A full Iris Recognition system was shown for improved recognition performance. Experiment findings on two benchmark iris datasets, IITD and MMU, showed that the suggested approaches for Iris Recognition performed well.

A multi-unit feature level fusion strategy for iris-based biometric systems was described in [ 13 ], with the goal of enhancing identification accuracy even for poorly segmented iris images. Based on the available research, it was obvious that greater attention should be placed on the pre-processing and segmentation stages for an iris-based biometric system to become trustworthy and accurate. Firstly, Daugman's Integro-Differential Operator was used for unconstrained eye images, and the iris area of interest was recovered without removing noise components, such as eyelid and eyelash occlusions, specular reflections, and so on. By working on missing values in badly segmented iris pictures, Probabilistic Principal Component Analysis (PPCA), a new feature selection technique, was applied to provide a high identification rate. To increase feature selection accuracy, the approach of multi-unit feature level fusion methodology was presented. When tested on the MMU dataset, the suggested technique achieved an 83.3% identification rate even for incorrectly segmented iris photos.

3 The Proposed Model for Iris Recognition

In this work, the proposed Iris Recognition system includes the following phases shown in Fig.  2 : (1) Reading Dataset, (2) Edge Detection, (3) Localization and Segmentation, and finally (4) Feature Extraction and Classification. The novelty of the proposed Iris Recognition system is the hybridization between the two techniques edge detection and segmentation, in which the image is first edge-detected and segmented to find all the edges, boundaries and circles in the eye image. After that, the output image of these phases goes into the feature extraction phase. This combination between edge detection and segmentation helps in the phase of feature extraction and classification, which makes the accuracy higher than using only one of the techniques before classification. Each phase will be discussed in the next sub-sections.

Sample images for a CASIA, b IIT Delhi, and c MMU, respectively

3.1 Dataset

Three datasets are used in building this model, which are CASIA-Iris-Interval V4, IITDelhi, and MMU, where all the images are greyscale. The resolutions for CASIA, IITD, and MMU datasets are 640 × 480, 320 × 240, and 320 × 240, respectively. Figure  2 shows image samples for the aforementioned datasets.

3.2 Edge Detection

Edge detection is a method in image processing that is used to determine the edges of objects inside images. It is carried out by taking into account the intensity variation that exists in one or more regions of an image. In applications, such as computer vision and image processing, edge detection is a very common problem. It is easy to see why we rely on edge detection for activities like depth perception and recognizing things in our range of view. Canny edge detection algorithm [ 14 ] is one of the most used algorithms in edge detection. It is used in the proposed model to detect the image's edges, which aids in the identification of objects, mostly circles. Canny's algorithm uses the first derivative for edge detection, taking intensity into account. In regions where the intensity does not change, a value of 0 is established, while in regions of a rapid intensity change, a value of 1 is established. This algorithm is divided into steps. Initially, the operators blur the image in the smoothing stage where the Gaussian filter has been applied to remove noise. Then, to acquire image gradients, the largest magnitude of the image gradient is computed using the step operator for marking the edges. Thereafter, the non-maximum suppression phase is used, in which the operator simply looks for local maxima and identifies them as edges, followed by a two-fold threshold approach with hysteresis to select the probable edge. Finally, a binary image is obtained, with either edged or non-edged pixels. In more detail, this binary edge map can be thought of as a set of edge curves that can be represented as polygons in the image domain. Edge detection applied to the used datasets is shown in Fig.  3 .

a Canny edge detection on CASIA. b Canny edge detection on IITD. c Canny edge detection on MMU

3.3 Localization and Segmentation

In general, a human eye has both dark and bright intensity regions. Because of the presence of such dark regions, pupillary border extraction is considered as a difficult process, due to the fact that the gray level intensities in these regions are often close to one another. Fortunately, these regions may be distinguished by their geometrical characteristics as well as substantial compactness of the pupil region. Both segmentation and localization are applied on a coarse iris region after the canny edge detection phase.

Image segmentation is a type of image processing method that divides an image into various portions sharing similarities, based on their attributes and qualities. In other words, it is commonly used to detect objects and boundaries in images. In this study, the Hough circle transform segmentation technique was used [ 15 ]. A Circular Hough transform is used twice: first to determine the iris/sclera boundary from the whole eye and again to determine the pupil/iris border from the iris area. Circular Hough transform will construct a circle in Hough space with varying radii for each edge point. The highest point in Hough space corresponds to the radius and center coordinates of the circle best delineated by the edge points. When compared to lines, circles are more simply represented in parameter space since circle parameters may be immediately transferred to the parameter space. As noted in the preceding section, Canny's method was used to identify the image's borders and to help in the identification of objects, usually circles, using the Hough transform. Edge detection was accomplished by taking into account the intensity fluctuation between one or more regions of an image. Following the identification of the edges, figures with a portion or whole circumference were recognized to locate the iris. Although it was only a partial circumference owing to things like eyelashes, eyelids, or the use of spectacles, it was the most visible within the image. Recognizing circumferences for iris detection was facilitated by the identification of edges. Table 1 shows the localization and segmentation phases applied to the aforementioned three datasets.

3.4 Feature Extraction and Classification

The extraction of the most discriminating features from an iris pattern is considered the most critical stage in the Iris Recognition process. The feature extraction phase has the greatest influence on the recognition rate of matching two iris templates. CNN is used in this study to extract features from a segmented iris image. To compare the similarity of two iris templates, there is a need for a matching metric. This matching measure determines if the two templates belong to the same or separate people. Herein, either CNN or HD is applied in feature extraction and classification phases. The input to the CNN or HD is the iris image after being edge-detected and segmented.

3.4.1 Convolutional Neural Network (CNN)

Two sub-datasets are used for creating the CNN model: one for training and the other for testing. The training dataset is classified into sub-folders, each sub-folder contains some iris images for one person where the CNN model searches for unique features in each one. This dataset is often much larger than the set of data for testing. Furthermore, the greater the quantity of training dataset, the higher the quality of the output using the trained dataset [ 13 ].

To discover different features, the machine uses the training dataset and layers aid in the extraction of features. In this situation, a layer indicates a specific process that turns the image into a different shape, size, color, or appearance using pixels that are much smaller than the real image. Many layers are added to images of the same category, which are subsequently saved in the CNN network. The stored features are based on the likelihood of several features from the training dataset being repeated. A generic layout of CNN is presented in this work. It is built on convolutional layers based on experimental analyses. Achieving an efficient and effective iris representation necessitates the creation of a good CNN architecture. In this regard, greyscale iris images with a resolution of 320 × 240 pixels are delivered into the established network which is extensive, with a high number of convolutional layers.

In CNN, each convolutional layer (conv1 to conv5) is followed by batch normalization. Pooling is often done after every two convolutional layers, and there is a total of four. The network is first built by stacking consecutive convolutional layers. Moreover, the first two pooling operations are performed after the convolutional layer, whereas additional pooling (pool3) is performed immediately after conv5. Usually, extremely tiny convolution kernels of size 3 × 8 (with stride of 2, padding of 'same') are used. Throughout the network, max-pooling is done with stride 2 across a 2 × 2-pixel frame. Each output neuron is coupled to all inputs in the top three layers, which are completely connected. Softmax classifier receives the output of the last fully connected layer. The learning rate is set to 0.01 for all datasets and then dropped by a factor of 10 when the validation error rate stops improving. In the training procedure, 15 epochs are employed with shuffling. I tried more than 15 epochs and the accuracy was not satisfactory. In all hidden layers, Rectified Linear Unit (ReLU) was applied as an activation function. During training, Stochastic Gradient Descent (SGD) is utilized to optimize the system and a back-propagation algorithm is used to calculate the gradients.

3.4.2 Hamming Distance (HD)

The hamming distance is used as a second method for feature extraction and classification, to determine if the two templates are created by heterogeneous iris or the same iris.

HD value resulted from comparing X and Y bits as the ratio of the number of different bits to the total number of bits in the template. ⊕ is the XOR operation and N is the feature length code. Xj and Yj are the identical bit code used to represent the two template image feature codes, respectively.

After the image is edge-detected and segmented, a feature template and an associated noise mask are created.

This template is compared to all of the final templates that have been enrolled, and a Hamming distance (HD) is calculated. While the iris templates created from separate irises are wholly unique, the values of the two-bit patterns are completely random, and the HD between them should be larger than or equal to 0.35, but the HD for the same iris templates should be close to 0 [ 4 ]. Our model predicts that the HD for iris images from the same eye is between 0.14 and 0.35 and for iris images from separate eyes is between 0.36 and 0.56. The HD range we employed was determined by the experiments described in the next section.

4 Experimental Results

The performance of the proposed systems is compared to previous literature based on recognition accuracy as shown in Tables 2 , 3 , 4 . The iris image is utilized as an input for feature extraction, and the classification process is performed using CNN or HD. The testing was performed on three publicly available datasets: IITD, CASIA-Iris-Interval V4, and MMU. The entire proposed scheme of Iris Recognition was implemented using MATLAB. To test the experiments, 600 jpeg iris images from the CASIA dataset with resolution 640 × 480, 639 bmp iris images from the IITD dataset with resolution 320 × 240, and 45 images from the MMU V1 dataset are used with resolution 320 × 240 in bmp format. To compute all accuracies, true positive matches and false negative matches are divided by the total number of picture samples in each dataset [ 13 ].

The proposed CNN classifier tested CASIA-Iris-Interval dataset achieved 91.56% in comparison with [ 3 ] which achieved 89% accuracy using Alex-Net and SVM as shown in Table 2 . When compared to other feature extraction models, the depth of the Alex-Net model is quite low, making it difficult to learn features from image sets and necessitating more time to attain higher accuracy. HD classifier was applied on the same dataset which achieved 94.88% accuracy compared to [ 4 ], which also used the same classifier, but our maximum HD was 0.35 compared to theirs which was 0.45, where the smallest HD is known to have a high probability of being correct. The HD used two kinds of filters [ 7 ]: The Gabor filter is a widely used approach for feature extraction. However, Legendre wavelet filter has nearly the same properties as Gabor filter and was designed based on the order of its polynomials, giving Legendre wavelet filter an advantage over Gabor filter. The Legendre Wavelet filter, like the Gabor Wavelet filter, is a linear texture analysis filter. Based on the efficiency and accuracy of their operations, the Legendre Wavelet filter was compared to the Gabor Wavelet filter, and a considerable improvement was achieved. They achieved lower accuracy than our enhanced one when applied to MMU and CASIA datasets. Radon transform and gradient-based isolation techniques used in [ 5 ] achieved 84.17% and 95.93% for CASIA and IITD datasets, respectively, compared to our results, 91.56% for CASIA and 96.56% for IITD using CNN.

The IITD dataset achieved 96.56% and 94.3% for the proposed classifiers which are higher compared to the following techniques as shown in Table 3 . The hybrid technique (CNN + KNN) [ 8 ] achieved 86%. CNN's performance with KNN was unsatisfactory. This is due to the fact that the KNN uses all of the data in the sample space, making classification harder with a bigger dataset. Compared to Dynamic Binary Particle Swarm Optimization used in [ 10 ], the results were somehow close to our CNN results, and when applied to the MMU dataset, they achieved 78.07%, which is lower than our classifiers applied on the same dataset as shown in Table 4 . When applied to the MMU dataset as shown in Table 4 , the testing results were 98.01% for the proposed CNN classifier compared to [ 11 , 12 ], and [ 7 ]. Our neural network architecture contains five layers to improve upon the previously mentioned techniques. For the same dataset, HD obtained 94.96% accuracy compared to [ 13 ], the accuracy being low due to the inaccurate segmentation of iris images which is a very important phase in Iris Recognition.

Figures  4 , 5 , 6 show the training and validation processes using CNN on CASIA-Interval, IITD, and MMU datasets. To build a CNN system, two datasets are needed: one for training and the other for testing. The computer searches for distinctive characteristics in each category using the training photos or train dataset. Layers aid in the extraction of characteristics and the stored features are based on the probability of several characteristics from the training dataset being repeated. When an image is evaluated, the same layers are added to it and then analyzed to decide which category best matches its features.

Accuracy of 91.56% of the CASIA-Iris-Interval dataset

Accuracy of 96.56% of the IITD dataset

Accuracy of 98.01% of the MMU dataset

All our accuracies are high due to the integration between the pervious phases which are edge detection using Canny edge detection and segmentation using HT in addition to the feature extraction phase using CNN. The classifier achieves high accuracy if the main phases of Iris Recognition are accurate, especially segmentation and feature extraction. Our hybrid techniques of using Canny edge detection and HT and then feature extraction helped the classifiers to achieve higher accuracy.

5 Conclusion

Iris Recognition is a difficult problem to solve in the imaging environment. As a result, we have suggested a hybrid technique for improving iris identification system performance in a noisy imaging environment, as well as increasing Iris Recognition rates on the CASIA-Iris-Interval, IITD, and MMU datasets. Accuracies are high owing to the interconnectivity of the previous steps, which include edge detection using Canny’s algorithm, segmentation using Hough transform, and feature extraction with CNN or HD. If the primary steps of iris identification, particularly segmentation and feature extraction, are precise, the classifier achieves high accuracy. Our hybrid technique of edge detection and segmentation, followed by feature extraction, assisted the classifiers in achieving greater accuracy. HD had high accuracy compared to the previous work because the iris image was processed in an accurate way in the edge detection and segmentation phases. The highest accuracy in HD is 94.88% when applied on Casia-Iris-Interval, and the CNN achieved 98.01% when applied on MMU dataset. In terms of recognition accuracy, the proposed system exceeds the compared ones.

Availability of data and material

All the datasets can be accessed from the internet.

Abbreviations

• Convolutional Neural Network

Fully Convolutional Network

• Hamming Distance

Binary Particle Swarm Optimization

Circular Hough Transform

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Aro, T.O., Jibrin, M.B., Matiluko, O.E., Abdulkadir, I.S., Oluwaseyi, I.O.: Dual feature extraction techniques for iris recognition system. I J Softw. Eng. Comput. Syst. 5 (1), 1–15 (2019)

Winston, J., Hemanth, D.J.: Moments-based feature vector extraction for iris recognition. International Conference on Innovative Computing and Communications, pp. 255–263. Springer, Singapore (2020)

Rafik, H. Djalal, M. Boubaker.: A Multi Biometric System Based on the Right Iris and the Left Iris Using the Combination of Convolutional Neural Networks. 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), pp. 1–10. IEEE, 2020.

Gowroju, Swathi, S. Kumar.: Robust Pupil Segmentation using UNET and Morphological Image Processing. 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 105–109. IEEE, 2021.

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Rahmatallah Hossam Farouk & Heba Mohsen

College of Computing & Information Technology, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt

Yasser M. Abd El-Latif

Faculty of Science, Ain Shams University, Cairo, Egypt

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RH carried out the experiments and drafted the manuscript, YM and HM participated in performing the analysis. The research study is written by all authors. All authors reviewed the results and approved the final version of the manuscript.

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Farouk, R.H., Mohsen, H. & El-Latif, Y.M.A. A Proposed Biometric Technique for Improving Iris Recognition. Int J Comput Intell Syst 15 , 79 (2022). https://doi.org/10.1007/s44196-022-00135-z

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Robust iris recognition using decision fusion and degradation modelling

Tomeo Reyes, Inmaculada (2015) Robust iris recognition using decision fusion and degradation modelling. PhD thesis, Queensland University of Technology.

Description

This thesis investigates the use of fusion techniques and mathematical modelling to increase the robustness of iris recognition systems against iris image quality degradation, pupil size changes and partial occlusion. The proposed techniques improve recognition accuracy and enhance security. They can be further developed for better iris recognition in less constrained environments that do not require user cooperation. A framework to analyse the consistency of different regions of the iris is also developed. This can be applied to improve recognition systems using partial iris images, and cancelable biometric signatures or biometric based cryptography for privacy protection.

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Latest Iris Recognition Thesis Topics

Iris recognition refers to the biometric modality which captures the human’s eye patterns. Different features such as Color, texture, and shape based information are necessary to identify the iris recognition. Other than the retina, sclera, pupils, eyelids, the iris is one of the unique and fastest biometric for authentication, verification, and identification. Both intrinsic and extrinsic features of eye patterns are used for recognition . This handout is framed with the very essential facts and concepts regarding the iris recognition thesis. 

As you know that, we are even being verified to access smart mobile phones and social media such as Instagram, Twitter, and so on. Iris recognition is one of the emerging technologies which have more reliability compared to finger vein recognition .

You could become a master in these areas by sailing with us throughout the article. Are you ready to sail with us? In fact, our technical crew has lighted up this article with the iris recognition fundamentals for the ease of your understanding. Doing research and writing the effective iris recognition thesis will abundantly bring you the results. Come on guys lets we get into the article!!!!

Iris Recognition Fundamentals

           Iris recognition is implemented using the set of images or videos captured in the WebCam. The patterns of the iris are extracted and stored in the storage system for identity verification. Then the real-time image is tested and entered into the system for authentication verification . In this case, the comparison is implemented between the trained images in the storage system and the test image. Based on that, the final result is produced. 

The above listed are the basic overview of fundamentals & how iris recognition is performed in real-time. In fact, iris recognition processes are done by gathering a massive amount of human eye patterns. It has biological features in which no one can do any malpractices. If you are feeling to get more information in these areas you can surely reach our technical crew at any time.

In the following passage, we’ve clearly mentioned to you the key features of iris recognition for the ease of your understanding . In fact, we felt that it is will be helpful to those students who are not aware of these areas of iris recognition. Are you interested in stepping into the next sections? We know you are tuned with the article flow. Come let’s have the quick insights!!!

 “Are you looking for an article regarding iris recognition thesis? Then this article is exclusively meant for those enthusiasts”

Key Features of Iris Recognition

• Touch-free authentication technology 
• Infrared camera capturing even in night times
• Recognition of iris even with eye-accessories
• Eye wise feature recognitions (left & right)
• Constant iris recognitions 
• Précised iris biometric results 
• Identical twins recognition

Iris recognition is one of the effective methods to identify human beings biometrically. In fact, they ensure hygienist authentication processes by offering contact-free (touch) authentications . They are much capable of recognizing eye patterns even in the night times and gloomy light conditions. 

As of now, we have seen the basic fundamentals and key features of iris recognition with clear hints. We hope that you would have understood the things till now illustrated. At this time, we felt that mentioning the steps involved in iris recognition would make you much wiser in the basic levels of iris recognition. Do you want to know them? Let’s keep tuned.

Steps for Iris Recognition

• Primarily, it acquires images of human eyes
• Secondly, it points out the eye/iris regions
• Thirdly, it dimensionally normalizes the iris regions
• Fourthly, it encodes the template with the sharp iris features
• Finally, it matches with the presented templates to identify the humans

If the template matching process doesn’t find any exact templates to the iris images given, then the person remains as unidentified. The above listed are the various steps involved in the processes of iris recognition. Iris segmentation processes are one of the challenging ones compared to other recognition stages. Iris segmentation is also done with the iris localization procedures. The next section is all about the iris segmentation techniques.  

Generally, iris segmentation techniques are dealt with by several features called machine learning, deep learning, manual, and other features . Let’s have further explanations in the upcoming section. Our technical team is focused on your understanding guys. Hence they listed the very needy points in each section of the article. Are you interested to know about the techniques of iris recognition ? Come let us get into the sections.

Iris Segmentation Techniques

• Hybrid- Attention, RANSAC & Domain Models
• Generative- Cycle-GAN & UNET
• Discriminative- Hierarchical / Fully CNN & CNN
• Watershed & Region Growing
• Active Contours
• Hough Transform & Edges
• Integro-Differential Operators 
• K-nearest Neighborhood 
• Local Binary Patterns
• Support Vector Machine 
• Gradient-based Histograms
• Graph Cuts & Markov Fields
• Standard Deviation & Bit Plane Vectors

The listed above are the various features that determine the iris localization techniques which are based on iris segmentation. A circle localization technique is also there to segment the iris features. If you do want their specifics you can feel free to approach our team. They are always there to assist you. By having sound knowledge in every area of technology they are highly capable of handling the technical issues that arise in iris recognition.

Yes, you guys guessed right! The next section is all about the issues in articulating iris recognition thesis. It is something important to note that actually. As we are engaged habitually with the experiments of the iris biometric processes we clearly know the issue that pops out in each and every approach. Come let’s have further explanations in the upcoming section .

Issues in Iris Recognition

• Iris Artifacts
• Low Lighting
• Impasse Distances
• Tilted Iris 
• Motion Occlusions
• Specular Replicas
• Blurred Eyelashes

The foregoing passage listed the issues in iris recognition . However, these constraints can be overcome by following several methodologies according to the nature of the issue. Here, we would like to enumerate the methods used to overcome the iris recognition issues for ease of your understanding. Come on readers, let’s also grab them!!!

Methodologies to Overcome Issues of Iris Recognition

• Un-constrained Segmentation Methods
• Non-Iris Occlusion Detecting Techniques
• Visible Wavelength Methods
• Gaussian Mixture Prototypes

These listed methods are widely used to eliminate the issues that arose in the iris recognition processes. On the other hand, our researchers in the industry are routinely investigating iris recognition approaches to eliminate the barriers arouse in the processes. These are the 4 major methods that are practiced in general. As well as our technical experts have listed out you the active research areas in iris recognition for the ease of your understanding.

Active Research Areas in Iris Recognition Thesis

• Enhanced Image Processing by Machine Learning
• Iris Recognition by Blockchain Methods
• Authorization by Multiple Components
• Iris Recognition by Hybrid Techniques
• Iris Recognition by Deep Learning Concepts
• Eye Image Preprocessing Methodologies
• Eye Image Acquirement Systems

The aforesaid are some of the active research areas in iris recognition. Apart from this, there is various research areas are presented. This is because iris recognition is one of the booming technologies which offers so many futuristic scopes while researching. Besides, our approaches are always based on the latest journals and articles.

  In the following passage, we have actually specified the latest iris recognition topics. Are you interested in stepping into the next phase? Yes, we know that you are already in the flow. Come let’s try to understand the same with crystal clear explanations.

Latest IRIS Recognition Thesis Topics

• FLD Neural Learning Authentications
• ELM & Fuzzy SVM Score Systems 
• Radial Function Identification
• AdaBoost & Deep Learning Recognition
• Game Theories & Phase based Segmentation
• Un-ideal & Incomplete Segmentation Methodologies
• Partial & Contour Segmentation Methodologies
• Neural Network Structures for Recognition
• Texture based Iris Pattern Segmentation 
• Level set & Convolutional Neural Network 
• Authentication by Gray Iris Features/Patterns
• DSP & DWT Feature-based Recognition 
• Recognition by HAAR Feature Extraction
• Detachment Methods for Cancelable Features
• Feature Extraction by Nonintrusive Techniques
• Feature-based Recognition by Multi-orientation Methods

The foregoing passage has revealed to you some of the recognition topics according to several aspects. In this regard, we would also like to mention the latest projects in iris recognition to make you understand. As we are always focused on the student’s welfare we are exposing all the possible aspects according to every technology. Now lets we move on to the next section.

Latest 2 Projects in Iris Recognition

• Used Algorithm:  Deep residual convolutional network & ResNet
• Processes:  Creates gradient flow to train the images according to the datasets
• Objective:  To create the shortcut links for recognition by skipping some layers 
• Outcomes:  Graphical representations of iris image classifications
• Used Algorithm:  Support Vector Machine 
• Processes:  Identifies & represents the iris features
• Objective : To improve the processes compared to other former approaches
• Outcomes:  Visualized iris recognition results

The bulletined above are the 2 latest projects in iris recognition. As well as there are plenteously amount of innovative projects and research ideas are with us. If you do want more information in accordance with this you can surely visit our websites and you can directly have interactions with our technical team for pattern recognition projects .

Generally, iris recognition ensures identity accuracy. In fact, it is highly compatible compared to other biometric technologies. In addition to this, it is also important to know the performance metrics that are used to evaluate the iris recognition processes . Yes, the next section is all about the important performance measures for iris recognition. 

Important Performance Measures for Iris Recognition

• Average Interference 
• Iris Data Losses
• Iris Pattern Reflection Rage
• Level of Iris Artifacts 
• Iris Pattern Blocking Rate
• Overlap Inter / Intra-class Hamming Distribution Average
• Overlap Inter / Intra-class Hamming Distribution

These are the measures that are used to evaluate the performance of iris recognition. We hope that you would have understood the concepts up to now stated. As this article is named an iris recognition thesis, here we are going to exhibit to you the steps to write the thesis. One can effortlessly express the perceptions of the handpicked approaches by projecting the effective thesis. Actually, the thesis is the best representation model of the researches executed. 

Generally, we do follow innovative structures to write the thesis. Our technical team summarized some of the tips to write a thesis for ease of your understanding . Let’s get into the next section. As this is one of the important sections, you are advised to pay your attention here. Come let’s try to understand them.

How to Write a Successful Thesis for Research? 

• Collect all the essential information according to your determined research
• Discover the novel topics by referring to the related articles & journals
• Itemize & handpick the ideas in the strongest areas of technical proficiency
• Form a rough summary of the thesis writing & split the chapters by section-wise
• Arrange the chapters ranging from ease from complex
• Refine and validate the research areas technically 
• Try to avoid copying others ideas & make proper usage of citations in the thesis
• In addition, avoid grammar mistakes in the areas of thesis writings
• Thoroughly reconsider the areas and make alterations if it is necessary

So far, we have discussed the iris recognition thesis and other concepts of the same with clear explanations. It is always advised; try to avoid using smart gadgets while writing a thesis or researching. If you are facing any challenges while writing the thesis youcan reach our experts at any time (24/7). We are always delighted to assist you in the areas of research.

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[TIFS 2020]Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition

xiamenwcy/IrisParseNet

Folders and files, repository files navigation, towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition.

Created by Caiyong Wang @ Institute of Automation, Chinese Academy of Sciences ( CASIA )

Call for participating in the IJCB 2021 Official Competition about NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) , welcome to visit our competition website: https://sites.google.com/view/nir-isl2021/home . Competition starting-ending: February 15 - April 30, 2021 . [ Registration closed! ]

We propose a high-efficiency deep learning based iris segmentation approach, named IrisParseNet . The proposed approach first applies a multi-task attention network to simultaneously predict the iris mask, pupil mask and iris outer boundary. Then, based on the predicted pupil mask and iris outer boundary, parameterized inner and outer iris boundaries are achieved by a simple yet effective post-processing method. Overall, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved, which facilitates the subsequent iris normalization as well as iris feature extraction and matching. Hence the proposed approach can be used for iris recognition as a general drop-in replacement. To help reproduce our method, we have made the models, manual annotations and evaluation protocol codes freely available to the community.

In the above figure, (a) original iris images from CASIA.v4-distance (top two), MICHE-I (middle three), and UBIRIS.v2 (bottom three) iris databases, (b) ground truth iris mask (blue), and inner (green) and outer (red) boundaries of the iris, (c) segmentation results of IrisParseNet (false positive error pixel (green), false negative error pixel (red), and true positive pixel (blue)), (d) iris outer boundary predicted by IrisParseNet, (e) pupil mask predicted by IrisParseNet, and (f) localization results of IrisParseNet after post-processing (inner boundary (green) and outer boundary (red)) are shown from left to right.

If you use this model or corresponding codes/datas for your research, please cite our papers.

Anyone is permitted to use, distribute and change this program for any non-commercial usage. However each of such usage and/or publication must include above citation of this paper. For any commercial usage, please send an email to [email protected] or [email protected] .

Prerequisites

• Python 2.7 ( python3 is not supported!)
• CPU or NVIDIA GPU + CUDA CuDNN
• matlab R2016a
• Halcon 10.0/13.0 or above

Getting Started

Installing caffe.

We have provided the complete Caffe codes. Just install it following the official guide. You can also refer to our another extended Caffe Version .

• Caffe [Google Drive] [Baidu Drive] (zuhv)

We have built an out-of-the-box Docker Caffe image ( https://www.codewithgpu.com/i/xiamenwcy/IrisParseNet/casia_caffe_tifs ) which is deployed on the AutoDL cloud server. You can enjoy it. There are no more worries stopping you from using IrisParseNet . 👏👏

Model training and testing

The complete codes for training and testing the model are placed at Codes/IrisParseNet , and the post-processing executable program is placed at Codes/Post-processing .

We have released the trained models on CASIA.v4-distance ( casia for short), MICHE-I ( miche for short), and UBIRIS.v2( nice for short).

• CASIA.v4-distance model [Google Drive] [Baidu Drive] (yavp)
• MICHE-I model [Google Drive] [Baidu Drive] (zpi4)
• UBIRIS.v2 model [Google Drive] [Baidu Drive] (376p)
• VGG_ILSVRC_16_layers.caffemodel [Google Drive] [Baidu Drive] (7ncn)

Evaluation protocols

The iris segmentation and localization evaluation codes are provided. During realizing the evaluation protocols, we've referenced a lot of open source codes. Here, we thank them, especially USIT Iris Toolkit v2 , TVM-iris segmentation , GlaS Challenge Contest .

Please read our paper for detailes. The evaluation codes can be found in evaluation folder.

Annotation codes

we use the interactive development environment ( HDevelop ) provided by the machine vision software, i.e. MVTec Halcon. Before labeling, you need to install Halcon software. Halcon is a paid software, but it allows to try out for free, please refer to the page: https://www.mvtec.com/products/halcon/now/ .

Our halcon based annotation codes can be found in annotation . The code can help us to label iris inner/outer bounadry and output a variety of kinds of annatation results as much as possible.

We have provided all training and testing datasets with ground truths to help reproduce our method. Since we do not have permission to release the original iris images for MICHE-I and UBIRIS.v2 databases, hence if you want to use the ground truths of these two databases, you can email the owners of both databases to request permission and let us know if given permission. We will provided the password of ground truth files.

Original iris database:

• MICHE-I Email: [email protected] .
• UBIRIS.v2 Email: [email protected] .

Ground truth:

• CASIA.v4-distance [Google Drive] [Baidu Drive] (kdd6)
• MICHE-I [Google Drive] [Baidu Drive] (84ya)
• UBIRIS.v2 [Google Drive] [Baidu Drive] (942d)

[1] Zhao, Zijing, and Ajay Kumar. "An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model." international conference on computer vision (2015): 3828-3836.

[2] Liu, Nianfeng, et al. "Accurate iris segmentation in non-cooperative environments using fully convolutional networks." Biometrics (ICB), 2016 International Conference on. IEEE, 2016.

[3] Hu, Yang, Konstantinos Sirlantzis, and Gareth Howells. "Improving colour iris segmentation using a model selection technique." Pattern Recognition Letters 57 (2015): 24-32.

[4] Proença H, Alexandre L A. The NICE. I: noisy iris challenge evaluation-part I[C]//2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. IEEE, 2007: 1-4.

[5] De Marsico M, Nappi M, Riccio D, et al. Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols[J]. Pattern Recognition Letters, 2015, 57: 17-23.

This package is only provided on "as it is" basis and does not include any warranty of any kind.

Please contact [email protected] or [email protected] .

Contributors 2

• MATLAB 31.2%
• Python 15.8%
• Makefile 1.8%

• [email protected]
• +91-97 91 62 64 69

IRIS RECOGNITION PROJECT

Iris recognition is a type of human verification technique that extracts the patterns of the iris by using some pattern recognition algorithms. For both image and video-based biometric authentication, iris recognition can be implemented. This article provokes innovative research ideas and gives more information about Iris Recognition Project !!!

Iris includes complex patterns that are unique, stable, and visible from a distance. Retinal scanning is a distinct ocular-based biometric technique that employs the distinct patterns on a person’s retina vascular system and is sometimes mistaken with iris recognition.  

How Does Iris Recognition Works? 

• Iris identification employs video camera technology with modest Near-Infrared Light (NIR) to capture photos of the iris. For this, the detail-rich, complicated structure is visible from the outside.
• The identification of an individual or someone claiming to be that individual is possible thanks to digital templates encoded from these patterns by mathematical and statistical algorithms.
• Matcher engines scan collections of enrolled templates at speeds measured in the millions of designs per second per (single-core) CPU , with very low false match rates.

Here we are given, How its work? This is made by our research students and this is a well-said method by them. From the above-specified areas, we have developed several for your projects. So we are continuously getting updates on all current trends of all kinds of research help. For your reference, here we have given our specialization in this project. So if you are interested to know more communicate with us.  

Flow for Iris Recognition

The right Eye and Left Eye are the two main characteristics of image acquisition. They are linked to the pre-processing step, which has two sections:

• Iris Localization
• Iris Normalisation

The primary one is CNN (Iris ConvNet), which has two characteristics. Match Score 1 and Match Score 2 , both of which are closely related to Rank Fusion and Decision Making.  

What are the applications of iris recognition? 

• It is a type of biometric technology similar to facial recognition and fingerprinting.
• Proponents of iris scanning technology say that it enables law enforcement personnel to compare suspects’ iris pictures to an existing database of photographs in order to verify or confirm the subject’s identity.

For students’ benefit, we are creating an easy section like, what is Iris Recognition? They can easily adapt the concept and throw the concept you will get a good future. We are here to make your comfortable features. These are the excellent ideas from our team. That ideas are should be always original and it has more future.

According to get unique ideas, perform a review on recent research papers related to the interested area. Then, figure out the current demands of the current research areas before finalizing your topic.  

Important Properties of Iris Recognition

Extrinsic properties

• Extrinsic characteristics have nothing to do with technology and are usually connected to the use case. For example, the effect of aging, pictures recorded in uncontrolled situations (for example, the specular reflections, effect of spectacles, outdoor imaging, and so on), and the potential of spoofing with an artificial iris.  

Intrinsic properties

• It is built into the technology or the acquisition procedure. For example, the iris’s collection spectrum (near-infrared (NIR) or visible), the area of the iris in the picture, and the sensor type (dedicated or integrated into mobile device)

Important properties are clearly described above for you. Now we can see that different subjects of Iris Recognition project that students mostly prefer this kind of project. We have included exactly what these areas cover in research-oriented projects. For your benefit, we support you in all these areas to create innovative research on the latest trends.

Research Gaps in IRIS Recognition

• Higher noise and sensitivity
• Non- universality & reliability
• Population coverage
• Intra / inter-class variability
• Vulnerability to spoofing     

  Where Iris Recognition works?

• Blockchain assisted Access Control
• Identity-based Authorization
• Multi-factor Authentication
• PUF-based Authentication
• Privacy Preserved Computation    

    You can get research help like assignment help , survey paper writing, conference paper writing, paper publications , and these kinds of writing services from us. Also, our engineers are here to guide you in all kinds of aspects like project design, algorithm writing, code implementation, and so on. It now becomes important to discuss algorithms for iris recognition, let’s look into the topic,  

Algorithms for Iris Recognition

Normalization

To avoid the artifacts and noise in the iris recognition, images are normalized. Pixel values i.e. color intensity and other types of techniques are applied to improve the normalization step. In order to facilitate the comparison of classification problems, the normalization procedure reduces artifacts. The main algorithms are,

• Normal score
• Standard score
• Coefficient of variation
• Min-Mix         

Segmentation

The picture must be precisely localized during the segmentation step so that the inner and outer edges of an iris may be represented as a circle. In the following, we highlighted some of the significant algorithms for iris recognition.

• Hough transform
• Daughman’s method
• Active Contour
• Linear basis function
• Adaptive level set  

Feature Detectors

After successfully normalizing and then segmenting the iris area, the following stage is to extract meaningful information from the normalized iris image. The retrieved characteristics are encoded in the iris template that is produced. The primary algorithms are as follows:

• Gabor filters
• Determinant of Hessian
• Wavelet transform  

Matching Techniques

At the test process, the templates created during the feature extraction stage are used to compare the similarity of two iris templates. This stage compares the similarity and dissimilarity of the two binary codes in order to make an acceptance or rejection judgment. The primary algorithms are as follows:

• Approximate Nearest Neighbours
• Overlap Similarity
• Pearson Similarity
• Cosine Similarity
• Jaccard Similarity
• Euclidean Distance

Machine learning approaches are used to concentrate on identification and feature extraction. Thus, the applications of machine learning and deep learning methodologies are increasing in medical image processing applications.

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Machine Learning Algorithms for Iris Recognition

• Support Vector Machine
• Decision Tree
• Fuzzy Logic
• K-Nearest neighbor
• Naïve Bayes Classifier

Classification, Clustering, and Dimensionality Reduction processes are considered for iris recognition, which can be solved and performed using machine learning. In this area, we have used two models: Low-Density Separation Models and Graph Bases Algorithms . Transfer learning has been used to a variety of computer vision issues, including image segmentation, image classification, super-resolution, emotion analysis, image captioning, face recognition, and object identification, and has considerably outperformed previous techniques.

Similar to machine learning algorithms, deep learning plays a significant role in iris recognition. The major purpose of using deep learning algorithms is used to support for huge volume of datasets. Further, this provides higher performance in terms of accuracy, precision, and f-score. 

Deep Learning Algorithms for Iris Recognition Project

• Radial Basis Function Networks (RBFNs)
• Multilayer Perceptions (MLPs)
• Deep Belief Network (DBNs)
• Restricted Boltzmann Machines ( RBMs)
• Autoencoders
• Convolution Neural Networks ( CNNs)
• Self-Organizing Maps (SOMs)
• Long Short Term Memory Networks (LSTMs)
• Recurrent Neural Networks ( RNNs)
• Generative Adversarial Networks ( GANs)

There are numerous public datasets with a respectable amount of samples for iris recognition tasks, but most of them have a restricted number of examples per class, making training challenging. In the next section, we present a significant volume of datasets.

In this arrangement, we gave the best one for your project. It shows the whole meaning of deep learning algorithms for iris recognition. After much research, our research team made the best list for your project. Moreover, we are also providing the research help concept like Iris Recognition with the help of our best team members.

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Datasets for Iris Recognition

CASIA-IrisV4-Lamp

It contains 16,212 images (819 classes) captured by a dedicated iris scanner (OKI Irispass-H). The images are captured in an indoor environment with lamps (visible light illumination of the rooms) both on and off.

CASIA-IrisV4-Thousand

• It has 2,000 classes that are collected by a specialized iris scanner (Irisking IKEMB -100).
• The images are obtained in an interior environment, similar to the CASIA-Iris V4- lamp, with lamps (visible light lighting of the rooms) both on and off. With over 1,000 individuals, this was the first publicly available iris database.  

IIT Delhi Iris Database (IITD-V1)

It is confined to Indian topics and comprises 1,120 NIR pictures (224 classes) recorded in a restricted context.  

IIT Delhi Iris Database (IITD-V2)

We can use our system through its paces on the IIT Delhi iris database, which comprises 2240 iris pictures from 224 distinct people. These pictures have a resolution of 320×240 pixels.  

The CUHK Iris Image Dataset

• The CUHK Based feature database comprises 254 NIR-captured pictures for 36 classes.
• This database was one of the earliest freely available iris datasets, however, it is rather tiny.  

CASIA-Iris V4- Interval

It comprises 2,639 images of 395 classes taken in an indoor setting using a bespoke NIR camera. The database is well-suited for studying the fine details from the iris texture.  

CASIA Iris Database v.1

• The National Laboratory of Pattern Recognition, Institute of Automation, CASIA database is created and collected from it.
• The database was acquired using a custom-built NIR camera, and the authors manually processed the images by replacing the pupil area (and specular reflections) with a constant intensity value.
• Because the manual involvement unduly simplified the situation, it is not suggested to utilize this database because it may be deceptive.
• The database has been continually updated since the original release of CASIAv.1 to the present CASIA v.4.
• Intra-class variations (CASIA-Iris-Lamp)
• Correlations in twins (CASIA-Iris-Twins)
• Influence of the capture distance (CASIA-Iris-Distance)
• Cross-sensor compatibility
• The influence of aging
• Unconstrained capture

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