Iris Recognition System Research Articles

A Hybrid Classification Approach for Iris Recognition System for Security of Industrial Applications

The biometric authentication system is demanded to identify a particular person from the set of persons. Even though many biometric authentication methods are available such as fingerprint, palm, face, and iris, the iris-based recognition system is effective due to its simplified process. This article proposes an iris recognition system using a hybrid classification approach for security applications. The proposed method includes three modules: preprocessing, augmentation, and classifier. The preprocessing module converts the color iris images into grey scale images and also resizes the image into 256 × 256. The preprocessed iris images are now data augmented to construct the larger dataset. The data augmented images are classified into either genuine or imposter images using a hybrid classification approach. The hybrid classification approach functions in two modes as training and testing. In this article, the Convolutional Neural Networks (CNN) is integrated with the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier to enhance the recognition rate of the iris recognition system. The performance analysis of the proposed approach is shown in terms of sensitivity, accuracy, recognition rate, specificity, false-positive rate, and false-negative rate. The experimental results of the proposed iris recognition system stated in this article significantly outweigh other design methods.

Iris recognition system using deep learning techniques

Iris recognition system using deep learning techniques

Toward an Efficient Iris Recognition System on Embedded Devices

Toward an Efficient Iris Recognition System on Embedded Devices

Effect of pupil dilation on biometric iris recognition systems for personal authentication.

To study the effect of pupil dilation on a biometric iris recognition (BIR) system for personal authentication and identification. A prospective, non-randomized, single-center cohort study was conducted on patients who reported for a routine eye check-up from November 2017 to November 2019 (2 years). An iris scanning device "IRITECH-MK2120U" was used to initially enroll the undilated eyes. Baseline scans were taken after matching with the enrolled database. All eyes were topically dilated and matched again with the enrolled database. The Hamming distance (a measure of disagreement between two iris codes) and recognition status were recorded from the device output, and eyes were evaluated by slit-lamp ophthalmoscopy with special emphasis on pupil shape, size, and texture. All 321 enrolled eyes matched after topical dilation. The pupil size had a significant effect on Hamming distance with a P value <0.05. There were no false matches. A correct recognition rate of 100% was obtained after dilation. No loss of iris texture or pupil shape was observed after dilation. A BIR system is a reliable method for identification and personal authentication after pupil dilation. Topically dilated pupils are not a cause for non-recognition of iris scans.

An effective authentication scheme for a secured IRIS recognition system based on a novel encoding technique

Iris recognition systems are well known for their accuracy than any other biometric system and very popular due to the permanent nature of the iris image. Moreover, it is easy to acquire the iris image even from a very long distance without causing much degradation to the image. In terms of the security perspective, very few biometric systems comprehensively address the vulnerabilities occurring as a result of security attacks. Among the security vulnerabilities, the possibility of compromise of Iris Code during the exchange of Iris information between client and server is a serious threat. In this work, an innovative model which generates a One Time Iris Code (OTIC) for every user authentication is proposed with the help of a novel encoding scheme. In the proposed model, the Iris Code generated distorted using pseudo-random number generator, timestamp, and rotation parameter and it will be different for each authentication session. The proposed technique will also reduce the possibility of compromising the original Iris Code. This will eventually enhance the security of the Iris recognition system. Moreover, the proposed method has less time complexity and high execution speed than the conventional Iris recognition systems. The accuracy of this model is comparable with the existing systems. Various performance analysis methods such as histogram processing for showing the pixel intensity of iris images, and calculation of EER based on entropy measurement for evaluating the severity of attacks using Base Score calculation have been implemented in this work.

Real-Time Human Authentication System Based on Iris Recognition

Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique part of eye which can never be altered, and it remains the same throughout the life of an individual. We have proposed the improved iris recognition system including image registration as a main step as well as the edge detection method for feature extraction. The PCA-based method is also proposed as an independent iris recognition method based on a similarity score. Experiments conducted using our own developed database demonstrate that the first proposed system reduced the computation time to 6.56 sec, and it improved the accuracy to 99.73, while the PCA-based method has less accuracy than this system does.

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

&lt;span&gt;Iris recognition system is a technique of identifying people using their distinctive features. Generally, this technique is used in security, because it offers a good reliability. Different researchers have proposed new methods for iris recognition system to increase its effectiveness. In this paper, we propose a new method for iris recognition based on Gabor filter and steerable pyramid decomposition. It’s an efficient and accurate linear multi-scale, multi-orientation image decomposition to capture texture details of an image. At first, the iris image is segmented, normalized and decomposed by Gabor filter and steerable pyramid method. Multiple sub-band are generated by applying steerable pyramid on the input image. High frequency sub-band is ignored to eliminate noise and increase the accuracy. The method was validated using CASIA-v4 (Chinese Academy of Sciences Institute of Automation), IITD (&lt;/span&gt;&lt;span&gt;Indian Institute of Technology Delhi) and UPOL (University of Phoenix Online) databases. The performance of the proposed method is better than the most methods in the literature. The proposed algorithm provides accuracy of 99.99%. False acceptance rate (FAR), equal error rate (EER) and genuine acceptance rate (GAR) have also been improved.&lt;/span&gt;

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach.

Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

A proposed framework for face - iris recognition system using enhanced mayfly algorithm

Fused biometrics systems have proven to solve some problems associated with unimodal systems but also face challenges in various aspects of their implementation such as difficulty in design, user acceptance is quite low, and the performance tradeoff. This framework tends to address some of these implementation challenges by using an enhanced mayfly algorithm, a modification of the existing mayfly algorithm that was recently proposed, as feature selection. Mayfly algorithm combines advantages of particle swarm optimization, genetic algorithm, and firefly algorithm, simulated in different experiments using varied benchmark function on conventional mayfly algorithm all tested to be capable of optimization, but despite its capabilities, some limitations such as slow convergent or premature convergent rate and possible imbalance between exploration and exploitation still remain unresolved, which necessitated enhancement for better performance. This framework will enhance the existing mayfly algorithm by expanding the search space which limited the ability of the conventional mayfly algorithm to be used to solve high-dimensional problem spaces such as feature selection and modify the selection procedure to model the attraction process as a deterministic process, that will be used for the feature selection procedure on fused face –iris recognition system. This will increase the capabilities of the mayfly algorithm and in turn, increase the recognition accuracy, and reduced the false acceptance rate, false rejection rate, and time complexity of the fused face–iris recognition system.

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

Identification of people using different biometric data is becoming more important in network society. Biometrics include voice, ears, palms, fingerprints, faces, iris, retina, and hand shapes. Among these features, iris detection gets more attention because each iris type is unique and does not change throughout life. In this study, an iris recognition framework is proposed using deformable circular hollow kernels and uniform histogram fusion images (UHFIs). This system introduces two different approaches for the iris recognition: one with image matching without machine learning and the other one with machine learning. In the first approach, image matching through Gabor features is employed. After getting the circularly cropped UHFI, Gabor features are extracted and employed in the image matching-based recognition system. In addition, a normalized cross-correlation coefficient is used as a similarity metric to compare the Gabor feature vectors. In the second approach, Gabor feature images are extracted and employed in deep learning (DL). According to the experimental results, the proposed system reaches around 89% accuracy on MMU1, 86% accuracy on MMU2, and about 50% accuracy on the CASIAV3 and CASIAV4 datasets when no machine learning is employed. Extensive benchmarking results also indicate that the proposed system boosts the performance of conventional systems by at least around 40% in terms of accuracy in the absence of machine learning. However, in the presence of machine learning, experimental results show that the proposed method with DL achieves an accuracy of up to 100% for MMU1, MMU2, CASIAV3, and CASIAV4 datasets.

LiSeNet: multitask lightweight segmentation network for accurate and complete iris segmentation

The demand for applying the iris segmentation model on mobile devices has been growing rapidly. Most current segmentation networks have an enormous amount of parameters, hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification networks and ignore the inherent characteristic of segmentation. To address the challenge, we propose a lightweight segmentation network (LiSeNet) for iris segmentation of noisy images. Unlike previous studies that only focus on improving the accuracy of segmentation masks, LiSeNet can simultaneously obtain segmentation masks, parameterized pupillary and limbic boundaries of the iris, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We first propose a multiscale concatenate (MSC) Block, which connects multiple sizes of convolution kernels in a dense manner, gradually reduces the dimension of feature maps and uses the aggregation of them for image representation. Based on the MSC block, we develop a two-stage refinement encoder to aggregate discriminative features through subnetwork feature reuse and substage feature reassess, thus obtaining a sufficient receptive field and enhancing the model learning ability. To exploit object contextual information more efficiently, we further devise a grouped spatial attention to emphasize the important features and suppress irrelevant noises through a gating mechanism in the decoder. Extensive experiments on three challenging iris datasets show that LiSeNet, without any complicated postprocessing, achieves competitive or state-of-the-art performance with only 2.2M parameters, being 14 × smaller than the previous best method. Code will be publicly available.

Noisy iris smoothing and segmentation scheme based on improved Wildes method.

In an automated iris recognition system, in order to get higher accuracy, we should have an efficient iris segmentation process. The reliability of accurate “iris recognition” system largely depends on the accuracy of segmentation process. Traditional “iris segmentation” methods are unable to detect the exact boundaries of iris and pupil, which is time consuming and also highly sensitive to noise. To overcome these problems, we have proposed an improved Wildes method (IWM) for segmentation in iris recognition system. The proposed algorithm consists of two major steps before applying Wildes method for segmentation: edge detection of iris and pupil from a noisy eye image with improved Canny with fuzzy logic (ICWFL) and removal of unwanted noise from above step with a hybrid restoration fusion filter (HRFF). A comparative study of various edge detection techniques is performed to prove the efficiency of ICWFL method. Similarly, the proposed method is tested with various noise densities from 10 to 95 dB. Also the working of the proposed HRFF is compared with some existing smoothing filters. Various experiments have been performed with the help of iris database of IIT_Delhi. Both visual and numerical results prove the efficiency of the proposed algorithm.

A Proposed Biometric Technique for Improving Iris Recognition

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.

Cancelable face and iris recognition system based on deep learning

Cancelable face and iris recognition system based on deep learning

Multi‐source heterogeneous iris segmentation method based on lightweight convolutional neural network

Iris segmentation is a critical step in the iris recognition system. Since the quality of iris database taken under different camera sensors varies greatly, thus most existing iris segmentation methods are designed for a particular collection device. Meanwhile, many iris segmentation methods based on convolutional neural networks (CNNs) require a lot of computational costs and hardware costs (storage space), which are not suitable for deploying on low-performance devices. To address the above problems, an accurate and efficient heterogeneous iris segmentation network is proposed in this paper. First, the authors design an efficient feature extraction network, which combines depth-wise separable convolution with traditional convolution to greatly reduce model parameters and computational cost while maintaining segmentation accuracy. Then, a Multi-scale Context Information Extraction Module (MCIEM) is proposed to extract multi-scale spatial information at a more granular level and enhance the discriminability of the iris region. Finally, a Multi-layer Feature Information Fusion Module (MFIFM) is proposed to reduce the loss of information during the downsampling process. Experimental results on multi-source heterogeneous iris database show that the proposed network can not only achieve state-of-the-art performance but is also more efficient in terms of required parameters, calculated load, and storage space.

Terrorist Distinguishment with Military Personnel on Attack in Air base Camps – 3 Chamber Approach: Using Real Time Monitoring – Arduino Sensor Detectors, Iris Recognition System &amp; Wireless Communicating Nodes Deployed

The security in the air base camps containing the nuclear assets and aircrafts has become a concern after interagency attacks from neighbouring countries. Manned Approach has proven to be of less efficiency in previous attacks. The research proposed 3C Chamber approach which is to be embedded into unmanned drones that will fly in the sky and recognise the terrorist intergencies. 1st chamber constitutes the 3 sensors connected with Arduino named EMAX 5300 which detects the explosives , DHT11 which is used to catch the humidity and body temperature of the running terrorists in the base camps and OV7670 is the Image sensor which will give the clear image and send to the second chamber which deals iris recognition with the image data so obtained and third chamber is basically the communication chamber that contains the database and deals with regional clusters in wireless communications , localisation of suspect area through network topology so mentioned in the later paper in form of clusters , alarm dissemination phase and to be time efficient we have time synchronisation in the third chamber. All the 3 chambers work simultaneously being connected to each other and respond to the centralised node with deals with action &amp; response. In the second chamber experiment is carried out with UBRIS.V1 database and OpenCv with Training : Testing ratios as 60:40, 50-50, 40-60 – the accuracy came out to be approximately 96.54%. Pre-registration phase involves the registration of the military personnel and later this data is retrieved and matched to catch and differentiate terrorists which is the main issue and concern of the research. The research is a combination of Biometrics , electronic sensors – internet of things , wireless network communication

Towards more accurate and efficient human iris recognition model using deep learning technology

In this study, an end-to-end human iris recognition system is presented to automatically identify individuals for high level of security purposes. The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a consumption time of 17.59 minutes for training set. While the testing accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.

Generalized Contact Lens Iris Presentation Attack Detection

The high accuracy of iris recognition for person identification has led to its deployment for a variety of applications ranging from border access to mobile unlocking to digital payment. In addition, the commercial success of mobile devices for iris image acquisition enables the easy acquisition of iris images both in an indoor controlled environment as well as an uncontrolled outdoor environment. At the same time, iris recognition systems can easily be attacked using wearable contact lenses. In the literature, several contact lens detection algorithms are proposed; however, the significant drawback is the generalizability under unseen testing domain images. In this research, a novel 3D contact lens iris presentation attack detection algorithm is developed and extensive experiments are performed. The experiments are performed using multiple challenging iris presentation attack databases including the IIITD and LivDet. For the evaluation, we have utilized the experimental protocols, which reflect in-the-wild settings for 3D contact lens iris presentation attack detection where the images belong to both controlled and adverse imaging conditions. The comparison with several state-of-the-art algorithms establishes the effectiveness of the proposed algorithm.

Real-time iris segmentation model based on lightweight convolutional neural network

Iris segmentation is an important task in the iris recognition system. Traditional iris segmentation methods are susceptible to irrelevant noise. At present, segmentation methods based on convolutional neural networks (CNNs) have become the main trend in addressing the task. Although these networks achieve promising segmentation performance on the advanced graphics processor, they require a lot of computation and time, which are far from meeting the standards of real-time iris segmentation. To this end, a precise and fast iris segmentation algorithm is proposed. First, an efficient feature extraction network that combines depth-wise separable convolution with dilated convolution is designed to reduce model parameters while maintaining segmentation accuracy. Then, an attention mechanism is introduced to suppress noise interference and enhance the discriminability of the iris region. Finally, an auxiliary training branch is proposed to overcome the vanishing gradient problem. Experimental results show that the proposed method not only achieves state-of-the-art performance but also is more efficient in terms of required parameters, calculated load, and storage space. Specifically, the model parameter is only 0.1 million (M), and the average prediction time is <0.03 seconds (S).

Performance Analysis of Feature Extraction Approach: Local Binary Pattern and Principal Component Analysis for Iris Recognition system

Many techniques have been proposed for the recognition of Iris. Most of them are single resolution techniques which results in poor performance. In this paper, feature extraction approaches like local binary pattern and principal component analysis assimilation has been offered. For classification, Support Vector Machine has been used. This paper compares the efficiency of two popular feature extraction methods Principal Component Analysis and Local Binary Pattern using two different iris databases CASIA and UBIRIS. The models were tested using 200 iris images. Statistical parameters like F1 score and Accuracy are tested for different threshold values. Our proposed method results with accuracy of 94 and 92%, is obtained for using Local Binary Pattern for CASIA and UBIRIS data set respectively. The Receiver Operating Characteristic Curve has been drawn and Area under Curve is also calculated. The experiment has been extended by varying the dataset sizes. The result shows that LBP achieves better performance with both CASIA and UBIRIS databases compared to PCA.