Fake Iris Research Articles

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection.

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.

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.

Presentation attack detection system for fake Iris: a review

The real-time solicitations of biometric systems have been extensively used for several things with the growing necessities of higher security level. There are numerous biometric traits used for person identification. In recent years, iris biometric trait become very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a real trait. The objective of this paper is to present a broad and well thought-out overview of the effort that has been conceded out over the preceding years in the field of iris anti-spoofing. Spoofing of an iris image can be done by paper print and contact lens etc. So, it is essential to discover fake iris up to its core level. In this survey, we have discussed various research work done in the past on the topic of different classes of presentation attack and summarized the state-of-the-art methods in a structural way. We have also discussed the future direction in the field of iris liveness detection.

A New Approach for Real-time Iris Liveness Detection Based on Eye Movement’s Features

This research introduces a new approach for iris liveness detection based on eye movement’s features. Given 50 valid eye frames from real-time video stream, this research identify them as the images that were taken whether directly from humans’ eyes or not. Our method ensures the selected images for further processing are eye ones without user’s cooperation during the sampling process. In addition, it also controls the application timeout by identifying either fake iris or low quality eye images. In my study, this Research used two features to model eye movements: (1) the width/height ratio of the eye outline and (2) the distance from the pupil center to the eye centroid. In order to model the acquired data distribution, this research used a bag of two-component Gaussian Mixing Models (GMMs). The class label of the acquired data depends on the GMMs which it belongs. The average accuracy of my proposed method on my and IriTech, Inc.’s databases was 94.704% and 100%, respectively. This result indicated that my method can make real-time iris-based systems work automatically with high security.

Databases for Iris Biometric Systems: A Survey

Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions.

Local binary hexagonal extrema pattern (LBHXEP): a new feature descriptor for fake iris detection

Security agencies frequently use biometric traits for automatic recognition of a person. The human iris is the most hopeful biometric authentication that can accurately identify a person from their exclusive features. However, in recent years, different types of spoofing attacks are used to violate the security of a biometric system. Biometrics liveness detection system used to recognize persons in a fast and trustworthy way through the use of unique biological distinctiveness. Presentation of a manufactured article of a human iris in the form of photo attack and contact lens attack could hamper the projected policy of a biometric system. The quality of real and fake iris images shows different textural characteristics. In this paper, we have proposed a novel and proficient feature descriptor, i.e., local binary hexagonal extrema pattern for fake iris detection. The proposed descriptor exploits the relationship between the center pixel and its Hexa neighbor. Hexagonal shape using “six-neighbor approach” is preferable to the rectangular structure due to its higher symmetry, consistent connectivity, and efficient use of space. The proposed consideration also solves the “curse of dimensionality” problem in liveness detection. The proposed descriptor is evaluated on ATVS-FIr DB and IIIT-D CLI databases for iris liveness detection and show promising performance for liveness detection in terms green, brown, etc. of accuracy and average error rate.

An approach for iris contact lens detection and classification using ensemble of customized DenseNet and SVM

Abstract In spite of the prominent advancements in iris recognition, it can significantly be deceived by contact lenses. As the contact lens wraps the iris region and obstructs sensors from capturing the actual iris. Moreover, cosmetic lenses are prone to forge the iris recognition system by registering an individual with fake iris signatures. Therefore, it is foremost to perceive the existence of the contact lens in human eyes prior to access an iris recognition system. This paper introduces a novel Densely Connected Contact Lens Detection Network (DCLNet) has been proposed, which is a deep convolutional network with dense connections among layers. DCLNet has been designed through a series of customizations over Densenet121 with the addition of Support Vector Machine (SVM) classifier on top. It accepts raw iris images without segmentation and normalization, nevertheless the impact of iris normalization on the proposed model’s performance is separately analyzed. Further, in order to assess the proposed model, extensive experiments are simulated on two widely eminent databases (Notre Dame (ND) Contact Lens 2013 Database and IIIT-Delhi (IIITD) Contact Lens Database). Experimental results reaffirm that the proposed model improves the Correct Classification Rate (CCR) up to 4% as compared to the state of the arts.

Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

Aim to countermeasure the presentation attack for iris recognition system, an iris liveness detection scheme based on batch normalized convolutional neural network (BNCNN) is proposed to improve the reliability of the iris authentication system. The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris, including convolutional layer, batch-normalized (BN) layer, Relu layer, pooling layer and full connected layer. The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels, and then the iris features are extracted by BNCNN. With these features, the genuine iris and fake iris are determined by the decision-making layer. Batch normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during training. Extensive experiments are conducted on three classical databases: the CASIA Iris Lamp database, the CASIA Iris Syn database and Ndcontact database. The results show that the proposed method can effectively extract micro texture features of the iris, and achieve higher detection accuracy compared with some typical iris liveness detection methods.

Feature Set Fusion for Spoof Iris Detection

Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database is 100% while it is 95.63% and 88.83% with IITD spoof iris databases respectively.

Detecting fake iris in iris bio-metric system

Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of the irises of an individual's eyes, whose complex random patterns are unique and can be seen from some distance. Now days, Iris is being used widely by several organizations, including governments, for identification and authentication purposes. Aadhar, India's UID project uses Iris scan along with fingerprints to uniquely identify people and allocate a Unique Identification Number. Most of the work done in the area of Iris pattern recognition systems emphasizes only on matching of the patterns with the stored templates. Security aspects of the system are still unexplored. The available security algorithms provide only some cryptographic solutions that keeps the template database in a secret cryptographic form. We successfully enhanced the detection of fake iris images and add the provision of detection of false of scanned iris images as template. This enhanced significantly the performance of the system in terms of security and reliability. We use Flash and motion detection of natural eye to detect the liveliness of real iris images before matching from stored templates.

Non‐reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

Non-reference image quality measures (IQM) as well as their associated natural scene statistics (NSS) are used to distinguish real biometric data from fake data as used in presentation/sensor spoofing attacks. An experimental study shows that a support vector machine directly trained on NSS as used in blind/referenceless image spatial quality evaluator provides highly accurate classification of real versus fake iris, fingerprint, face, and fingervein data in generic manner. This contrasts to using the IQM directly, the accuracy of which turns out to be rather data set and parameter choice-dependent. While providing very low average classification error rate values for complete training data, generalisation to unseen attack types is difficult in open-set scenarios and obtained accuracy varies in almost unpredictable manner. This implies that for each given sensor/attack set-up, the ability of the introduced methods to detect unseen attacks needs to be assessed separately.

Entropy with Local Binary Patterns for Efficient Iris Liveness Detection

Iris anti-spoofing is one of the most important topics, in which the development is increasing rapidly. This paper introduces an efficient system for detecting iris attacks. The system avoids the segmentation and the normalization stages employed traditionally in fake detection systems. Wavelet packets (WPs) are used to decompose the original image into wavelet approximation and detail channels. Entropy values are extracted from the wavelet channels, and also from the local binary pattern (LBP) images of the channels. These features are used for discriminating between real and fake iris images. Support vector machines are used for the classification purpose. The aim is to contribute for improved classification accuracy with less computational complexity and reduced processing time. Entropy of the WP channels gives 99.9237% classification accuracy, and the entropy of the LBP images yields 99.781%, using ATVS-FIr-DB. Fusion of these features yields 100% classification accuracy. Entropy of the wavelet channels is sufficient to obtain 100% accuracy using CASIA-Iris-Syn database, without fusion. All images in both databases are used, without the need to discard images with unsuccessful segmentation. Segmented images from both databases are used for comparison. Results show that more discriminative features can be obtained using the proposed algorithm. System complexity and processing time are reduced noticeably, and the system is robust to different types of fakes.

Iris Image Classification Based on Hierarchical Visual Codebook

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

Liveness detection for iris recognition using multispectral images

Liveness detection is a necessary step towards higher reliability of iris recognition. In this research, we propose a novel iris liveness detection method based on multi-features extracted from multispectral images. First, we analyze the specific multispectral characteristics of conjunctival vessels and iris textures. To ensure the effective utilization of these characteristics, iris images are simultaneously captured at near-infrared (860nm) and blue (480nm) wavelengths. Then we respectively define and measure relative number of conjunctival vessels (RNCV) and entropy ratio of iris textures (ERIT) using 860-nm and 480-nm images. Finally, the feature values of RNCV and ERIT are arranged to form a robust 2-D feature vector. The trained Support Vector Machine (SVM) is used to classify the feature vectors extracted from live and fake irises. Experimental results demonstrate that the proposed method can discriminate between live irises and various types of fake irises with high classification accuracy and low computational cost.

Iris Recognition for Personal Identification System

In general, a typical iris recognition based Personal Identification System (PIS) includes iris imaging, iris image quality assessment, fake iris detection, and iris recognition. This paper presents a novel approach, which focusing on iris recognition. The novelty of this approach includes improving the speed and accuracy of the iris segmentation process, fetching the iris image so as to reduce the recognition error, producing a feature vector with discriminating texture features and a proper dimensionality so as to improve the recognition accuracy and computational efficiency. The Canny edge detection and circular Hough transforms are used for the segmentation process. The segmented iris is normalized using Daugman's rubber sheet model from –[32°,32°] and [148°,212°]. The phase data from 1D Log-Gabor filter is extracted and encoded efficiently to produce a proper feature vector. Experimental tests were performed using CASIAIrisV3 and UBIRIS iris databases. These tests prove that the proposed algorithm has an encouraging performance.

Anti-Spoofing Method for Iris Recognition by Combining the Optical and Textural Features of Human Eye

In this paper, we propose a fake iris detection method that combines the optical and textural features of the human eye. To extract the optical features, we used dual Purkinje images that were generated on the anterior cornea and the posterior lens surfaces based on an analytic model of the human eye’s optical structure. To extract the textural features, we measured the amount of change in a given iris pattern (based on wavelet decomposition) with regard to the direction of illumination. This method performs the following two procedures over previous researches. First, in order to obtain the optical and textural features simultaneously, we used five illuminators. Second, in order to improve fake iris detection performance, we used a SVM (Support Vector Machine) to combine the optical and textural features. Through combining the features, problems of single feature based previous works could be solved. Experimental results showed that the EER (Equal Error Rate) was 0.133%.

Fake iris detection based on 3D structure of iris pattern

A new fake iris detection method based on 3D feature of iris pattern is proposed. In pervious researches, they did not consider 3D structure of iris pattern, but only used 2D features of iris image...

Fake iris detection based on 3D structure of iris pattern

AbstractA new fake iris detection method based on 3D feature of iris pattern is proposed. In pervious researches, they did not consider 3D structure of iris pattern, but only used 2D features of iris image. However, in our method, by using four near infra‐red (NIR) illuminators attached on the left and right sides of iris camera, we could obtain the iris image in which the 3D structure of iris pattern could be shown distinctively. Based on that, we could determine the live or fake iris by wavelet analysis of the 3D feature of iris pattern. Experimental result showed that the Equal Error Rate (EER) of determining the live or fake iris was 0.33%. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 162–166, 2010

Fake iris detection method using Purkinje images based on gaze position

Until now, most research on iris recognition has been focused on recognition algorithms and iris camera systems. There has been little research into fake iris detection, although recently its importance has been greatly emphasized. Fake iris detection refers to the process of detecting and defeating fake iris images. In this work, we propose a new method of defeating fake iris attacks using Purkinje images based on gaze position. Our research presents the following four improvements over previous works. First, we calculate the theoretical positions and distances between the Purkinje images based on the 3-D human eye model. Second, by using these positions and distances (which changed according to gaze positions), we design a more robust way of detecting fake irises. Third, since it is not necessary to align the center of the user's eyeball with the optical axis of the camera, the proposed method can be used in practical iris systems. Fourth, by activating the illumination infrared-light-emitting diode (IR-LED), the distance-measuring IR-LED and the Purkinje IR-LED alternatively, we obtain accurate positions for the Purkinje images according to the user's gaze position. Experimental results show that the false rejection rate (FRR) is 0.2% and the false acceptance rate (FAR) is 0.2%.

Multifeature-based fake iris detection method

The purpose of fake iris detection is to discriminate between real and fake iris images and to defeat fake (forged) iris images. A robust fake iris detection method should be able to detect various types of fake iris images obtained from a fake printed iris, an artificial eye, or a fake contact lens, correctly and nonintrusively. To solve the problem, we propose a new fake iris detection method. We measure distinctive physiological multifeatures [the first and second features refer to the reflectance ratios of the iris to the sclera (RRIS) at 750 and 850 nm, respectively, and the third feature refers to the thickness of the corneoscleral limbus], and classify those features extracted from live irises and fake irises using a support vector machine (SVM). Using the proposed method, we can discriminate various types of fake iris images without inconveniencing users by shining visible light. To measure the performance of the method, three types of fake irises are made: a printed iris, an artificial eye, and a fake contact lens. Our experimental results show that it is possible to detect those fake iris images with high accuracy.