A Framework to Detect Presentation Attacks

Biometric-based authentication systems are becoming the preferred choice to replace password-based authentication systems. Among several variations of biometrics (e.g., face, eye, fingerprint), iris-based authentication is commonly used in every day applications. In iris-based authentication systems, iris images from legitimate users are captured and certain features are extracted to be used for matching during the authentication process. Literature works suggest that iris-based authentication systems can be subject to presentation attacks where an attacker obtains printed copy of the victim’s eye image and displays it in front of an authentication system to gain unauthorized access. Such attacks can be performed by displaying static eye images on mobile devices or iPad (known as screen attacks). As iris features are not changed, once an iris feature is compromised, it is hard to avoid this type of attack. Existing approaches relying on static features of the iris are not suitable to prevent presentation attacks. Feature from live Iris (or liveness detection) is a promising approach. Further, additional layer of security from iris feature can enable hardening the security of authentication system that existing works do not address. To address these limitations, this thesis proposed iris signature generation based on the area between the pupil and the cornea . Our approach relies on capturing iris images using near infrared light. We train two classifiers to capture the area between the pupil and the cornea. The image of iris is then stored in the database. This approach generates a QR code from the iris. The code acts as a password (additional layer of security) and a user is iii required to provide it during authentication. The approach has been tested using samples obtained from publicly available iris database. The initial results show that the proposed approach has lower false positive and false negative rates

Emerging biometric technologies for Automated Border Control gates

Automated Border Control (ABC) gates, or shortly e-Gates, are systems able to verify automatically the identity of the travelers through the biometric traits, and to grant passage of the border. Biometric technologies make the clearance automation possible, with a positive impact on efficiency, effectiveness, security, and usability of the process. The e-Gate compares biometric data of the traveler from an electronic document against live acquisitions, using different biometric traits. The face emerged in this area as the primary trait used by the e-Gates, with fingerprint and iris more adopted in registered traveler programs. This paper analyzes the main biometric aspects relating to both the human-machine interaction and the technologies used for ABC, and presents the emerging solutions that can produce a performance enhancement

Enhancing fingerprint biometrics in Automated Border Control with adaptive cohorts

Automated Border Control (ABC) systems are being increasingly used to perform a fast, accurate, and reliable verification of the travelers' identity. These systems use biometric technologies to verify the identity of the person crossing the border. In this context, fingerprint verification systems are widely adopted due to their high accuracy and user acceptance. Matching score normalization methods can improve the performance of fingerprint recognition in ABC systems and mitigate the effect of non-idealities typical of this scenario without modifying the existing biometric technologies. However, privacy protection regulations restrict the use of biometric data captured in ABC systems and can compromise the applicability of these techniques. Cohort score normalization methods based only on impostor scores provide a suitable solution, due to their limited use of sensible data and to their promising performance. In this paper, we propose a privacy-compliant and adaptive normalization approach for enhancing fingerprint recognition in ABC systems. The proposed approach computes cohort scores from an external public dataset and uses computational intelligence to learn and improve the matching score distribution. The use of a public dataset permits to apply cohort normalization strategies in contexts in which privacy protection regulations restrict the storage of biometric data. We performed a technological and a scenario evaluation using a commercial matcher currently adopted in real ABC systems and we used data simulating different conditions typical of ABC systems, obtaining encouraging results

Human Iris Polymorphisms: Computer–based and Genetic Assessments of Human Irises and Possible Applications in Human Identification

During personal identification we can analyse the phenotypic or genotypic complexions of a human. The ancient histories of scientific activities in this field were related to the descriptive or measurable features, called phenotype analyses. In the last decades of the 20th century, during the era of human genetics, numerous polymorphic genetic markers were discovered investigating the human nuclear or mitochondrial DNA (deoxyribonucleic acid). The results of the Human Genome Project revolutionized the applications and opened an era of the investigations for externally visible characteristics (EVCs), the so called DNA based phenotyping (age, hair–, and eye– colour investigations) using informative molecular markers. The polymorphic characteristics of the human eye are well known. This partly originates from the vessel network and the layer order of the retina or the unique construction of the initial section of the optic nerve at the eye-ground. The iris’ individuality resides in its complex textural construction. The iris’ colour and partly its patterns (variations of the Fuchs’ crypts, nevi dots, Wolfflin nodules and contraction furrows) are genetically determined. All of these previously mentioned iris polymorphisms led to the development of a number of automatic phenotypic or genotypic biometric personal identification practical applications. The aim of this study is to briefly summarize the background of this topic condensing those results which are available in this field, and to present our efforts related to a novel approach in the field of iris colour prediction

Iris recognition using deep learning

Despite the large increase of deep learning solutions in recent years, no deep learning iris pipelines have yet been developed. Inspired by conventional iris recognition pipelines, we present our general deep architecture for iris recognition. The presented deep iris pipeline is an end-to-end convolutional neural network consisting of two high-level blocks: segmentation and recognition. The segmentation part is tasked with the generation of binary mask, which corresponds with the surface of the iris. These masks are multiplied with the original iris image and then fed to the recognition part. The recognition part extracts meaningful iris features, which are then used for matching. Our model achieved high results on both testing datasets. On Casia-Iris-Thousand it achieved a Rank-1 accuracy of 95.12% and on SBVPI an accuracy of 92.33%. We also implemented a cross-database model, trained on samples from both dataset, which achieved an accuracy of 88.53%. Our deep pipeline outperformed a conventional iris pipeline in speed and accuracy. As far as we are aware, our pipeline is the first implementation of an end-to-end deep neural network, which is able to segment and recognize the iris image. As opposed to current deep models, which perform recognition on a pre-normalized iris image, our method uses original iris images

Biometric recognition in automated border control : a survey

The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas

Activity-Based User Authentication Using Smartwatches

Smartwatches, which contain an accelerometer and gyroscope, have recently been used to implement gait and gesture- based biometrics; however, the prior studies have long-established drawbacks. For example, data for both training and evaluation was captured from single sessions (which is not realistic and can lead to overly optimistic performance results), and in cases when the multi-day scenario was considered, the evaluation was often either done improperly or the results are very poor (i.e., greater than 20% of EER). Moreover, limited activities were considered (i.e., gait or gestures), and data captured within a controlled environment which tends to be far less realistic for real world applications. Therefore, this study remedies these past problems by training and evaluating the smartwatch-based biometric system on data from different days, using large dataset that involved the participation of 60 users, and considering different activities (i.e., normal walking (NW), fast walking (FW), typing on a PC keyboard (TypePC), playing mobile game (GameM), and texting on mobile (TypeM)). Unlike the prior art that focussed on simply laboratory controlled data, a more realistic dataset, which was captured within un-constrained environment, is used to evaluate the performance of the proposed system. Two principal experiments were carried out focusing upon constrained and un-constrained environments. The first experiment included a comprehensive analysis of the aforementioned activities and tested under two different scenarios (i.e., same and cross day). By using all the extracted features (i.e., 88 features) and the same day evaluation, EERs of the acceleration readings were 0.15%, 0.31%, 1.43%, 1.52%, and 1.33% for the NW, FW, TypeM, TypePC, and GameM respectively. The EERs were increased to 0.93%, 3.90%, 5.69%, 6.02%, and 5.61% when the cross-day data was utilized. For comparison, a more selective set of features was used and significantly maximize the system performance under the cross day scenario, at best EERs of 0.29%, 1.31%, 2.66%, 3.83%, and 2.3% for the aforementioned activities respectively. A realistic methodology was used in the second experiment by using data collected within unconstrained environment. A light activity detection approach was developed to divide the raw signals into gait (i.e., NW and FW) and stationary activities. Competitive results were reported with EERs of 0.60%, 0% and 3.37% for the NW, FW, and stationary activities respectively. The findings suggest that the nature of the signals captured are sufficiently discriminative to be useful in performing transparent and continuous user authentication.University of Kuf