Conventional Iris Recognition Systems Research Articles

LRFID-Net: A Local-Region-Based Fake-Iris Detection Network for Fake Iris Images Synthesized by a Generative Adversarial Network

Iris recognition is a biometric method using the pattern of the iris seated between the pupil and the sclera for recognizing people. It is widely applied in various fields owing to its high accuracy in recognition and high security. A spoof detection method for discriminating a spoof attack is essential in biometric recognition systems that include iris recognition. However, previous studies have mainly investigated spoofing attack detection methods based on printed or photographed images, video replaying, artificial eyes, and patterned contact lenses fabricated using iris images from information leakage. On the other hand, there have only been a few studies on spoof attack detection using iris images generated through a generative adversarial network (GAN), which is a method that has drawn considerable research interest with the recent development of deep learning, and the enhancement of spoof detection accuracy by the methods proposed in previous research is limited. To address this problem, the possibility of an attack on a conventional iris recognition system with spoofed iris images generated using cycle-consistent adversarial networks (CycleGAN), which was the motivation of this study, was investigated. In addition, a local region-based fake-iris detection network (LRFID-Net) was developed. It provides a novel method for discriminating fake iris images by segmenting the iris image into three regions based on the iris region. Experimental results using two open databases, the Warsaw LiveDet-Iris-2017 and the Notre Dame Contact Lens Detection LiveDet-Iris-2017 datasets, showed that the average classification error rate of spoof detection by the proposed method was 0.03% for the Warsaw dataset and 0.11% for the Notre Dame Contact Lens Detection dataset. The results confirmed that the proposed method outperformed the state-of-the-art methods.

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.

Cancellable iris template generation based on Indexing-First-One hashing

Eye iris has been widely recognized as one of the strongest biometrics attributed to its high accuracy performance. However, templates in conventional iris recognition systems are unprotected and highly vulnerable to numerous security and privacy attacks. Despite a number of cancellable biometric schemes have been proposed but at the expense of substantially decreased accuracy performance. In this paper, we introduce a new cancellable iris scheme, coined as “Indexing-First-One” (IFO) hashing. IFO hashing is inspired from the Min-hashing that primarily used in text retrieval domain. However, IFO hashing has been further strengthened by two novel mechanisms, namely P-order Hadamard product and modulo threshold function. The IFO hashing scheme strikes the balance between accuracy performance and privacy/security protection. Comprehensive experiments on CASIA-v3 iris benchmark database and rigorous analysis demonstrate decent accuracy performance with respect to its original counterparts yet offer strong resilience against several major security and privacy attacks.

Extending the Capture Volume of an Iris Recognition System Using Wavefront Coding and Super-Resolution.

Iris recognition has gained increasing popularity over the last few decades; however, the stand-off distance in a conventional iris recognition system is too short, which limits its application. In this paper, we propose a novel hardware-software hybrid method to increase the stand-off distance in an iris recognition system. When designing the system hardware, we use an optimized wavefront coding technique to extend the depth of field. To compensate for the blurring of the image caused by wavefront coding, on the software side, the proposed system uses a local patch-based super-resolution method to restore the blurred image to its clear version. The collaborative effect of the new hardware design and software post-processing showed great potential in our experiment. The experimental results showed that such improvement cannot be achieved by using a hardware-or software-only design. The proposed system can increase the capture volume of a conventional iris recognition system by three times and maintain the system's high recognition rate.

Noncooperative bovine iris recognition via SIFT

Iris biometry has been widely used to recognize an individual in a natural and intuitive way. Conventional iris recognition systems transfer iris images to rectangle using polar coordinate after accurate segmentation, and have performed very well on accurate data. However, bovine iris images are usually irregular with respect to inactive participant, and the conventional methods cannot achieve high accuracy and true rotation invariance. In this paper, a new scheme is proposed based on scale invariant feature transform (SIFT) and bag-of-features. Firstly, region-based active contour is used to detect the inner boundary. Secondly, SIFT method is applied to detect the keypoints in the iris image, and points located in pupil region are removed. Then, feature vocabulary is constructed, and histogram representation for each iris image is generated. Finally, histogram distance is adopted for the matching test. Experimental results are provided to show the effectiveness and potential of developed noncooperative iris recognition.

Iris recognition system based on video for unconstrained environments

This paper proposes an iris image quality and segmentation accuracy evaluation method for video-based iris recognition systems, operating in unconstrained environment. Proposed approach consists of two stages of video quality evaluation that allows improving the iris recognition rates in non-ideal or no cooperative situations; where the first stage discards the low quality eye frames, while the second stage discards frames with low quality iris segmentation providing properly segmented iris frames to carry out the unconstrained iris recognition task. Although proposed scheme was evaluated using the Daugman algorithm, it may be used with several other iris recognition systems operating in constrained environments.Evaluation results show that the performance of conventional iris recognition system, using the proposed scheme, reduces the equal error rate (EER) value in about 12.2%. Key words: Biometric systems, video-based iris recognition, image quality measure, non-ideal iris frames, segmentation accuracy evaluation, unconstrained environments.

Iris recognition based on score level fusion by using SVM

In conventional iris recognition methods, due to the difficulty of selecting one optimal wavelet filter for iris feature extraction, multiple wavelet filters (with different frequencies and kernel sizes) are adopted. However, this causes the processing time and the extracted feature size to increase. To overcome this problem, feature level fusion of the extracted iris features has been proposed, but this method requires a complicated dimension reduction procedure. Therefore, we propose a new iris recognition method based on score level fusion, using two Gabor wavelet filters and SVM (support vector machine). For score level fusion, we used the typical HD (Hamming distance) produced by a Gabor filter, which can easily be applied to conventional iris recognition systems. The proposed method has three novelties compared to previous works. First, when generating iris feature codes, we excluded detected eyelid, eyelash and SR (specular reflection) regions, which act as noise factors in iris feature extraction. Second, for enrollment, we checked the number of reliable iris feature codes that were not generated from the eyelid, eyelash and SR occluded regions. Only if the number of reliable codes exceeded in the predetermined threshold, we performed enrollment with high confidence, which reduced the FRR (false rejection rate). Third, two Gabor filters were used for local and global iris textures and the HDs calculated by those Gabor filters were fused by the SVM and the consequent authentication error was greatly reduced. Experimental results showed that the authentication error of the proposed method was much smaller than the authentication errors when using the single Gabor filter, the filter-bank, the score level and the decision level fusion methods.

System of the Real-Time Acquisition and Recognition for Iris Images

Iris recognition is to identify a user based on the iris texture information which exists between the white sclera and the black pupil. Iris recognition system has been in the limelight for high-security biometric applications due to the advantages of non-contact characteristics and the highest recognition performance among biometric systems. Conventional iris recognition systems consist of the iris camera and the processing unit, like a PC or an embedded control box. The iris camera captures the user's iris images and transfers them to the processing unit. In the processing unit, the captured images are processed and recognition is performed. For fast recognition, it is very important to capture the user's focused eye image at fast speed. If not, the total recognition time is increased and it makes the user feel much inconvenience. In previous researches and systems, they use the focusing method which has been used for general landscape scenes without considering the characteristics of iris image. So, they take much focusing time sometimes, especially in the case of the user with glasses. To overcome such a problem, we propose a new iris image acquisition method to capture the user's focused eye image at very fast speed. It can be applicable to the users both with and without glasses.