An official website of the United States government
Reliability and accuracy of iris biometric modality has prompted its large-scale deployment for critical applications such as border control and national ID projects. The extensive growth of iris recognition systems has raised apprehensions about susceptibility of these systems to various attacks. In the past, researchers have examined the impact of various iris presentation attacks such as textured contact lenses and print attacks. In this research, we present a novel presentation attack using deep learning based synthetic iris generation. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, we propose a new framework, named as iDCGAN (iris deep convolutional generative adversarial network) for generating realistic appearing synthetic iris images. We demonstrate the effect of these synthetically generated iris images as presentation attack on iris recognition by using a commercial system. The state-of-the-art presentation attack detection framework, DESIST is utilized to analyze if it can discriminate these synthetically generated iris images from real images. The experimental results illustrate that mitigating the proposed synthetic presentation attack is of paramount importance.
more »
« less
Iris Print Attack Detection using Eye Movement Signals
Iris-based biometric authentication is a wide-spread biometric modality due to its accuracy, among other benefits. Improving the resistance of iris biometrics to spoofing attacks is an important research topic. Eye tracking and iris recognition devices have similar hardware that consists of a source of infra-red light and an image sensor. This similarity potentially enables eye tracking algorithms to run on iris-driven biometrics systems. The present work advances the state-of-the-art of detecting iris print attacks, wherein an imposter presents a printout of an authentic user’s iris to a biometrics system. The detection of iris print attacks is accomplished via analysis of the captured eye movement signal with a deep learning model. Results indicate better performance of the selected approach than the previous state-of-the-art.
more »
« less
- Award ID(s):
- 1714623
- PAR ID:
- 10393866
- Date Published:
- Journal Name:
- 2022 Symposium on Eye Tracking Research and Applications
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Iris biometric systems offer non-contact authentication, particularly advantageous in controlled environments such as security checkpoints. However, challenges arise in less controlled scenarios such as standoff biometrics where captured images mostly are non-ideal including off-angle. This paper addresses the need for iris recognition models adaptable to various gaze angles by proposing a blink detection algorithm as an additional feature. The study explores different blink detection methods including involving logistic regression, random forest, and deep learning models. For the first methodology, logistic regression and a random forest model were used to classify eye images into four different blink classes. The second methodology involved labeling eye openness percentage. The ground-truth eye blink was calculated using facial landmarks detected by the MediaPipe model. For the deep learning approach, we used a pre-trained Convolutional Neural Network (CNN) model by replacing the output layer with a regression layer. Results show improved precision and recall when incorporating height and width features for the regression model. The AlexNet model achieves superior performance, reaching 90% accuracy with a 10 % error threshold. This research contributes valuable insights for developing robust iris recognition models adaptable to diverse gaze angles.more » « less
-
Biometric databases collect people's information and allow users to perform proximity searches (finding all records within a bounded distance of the query point) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric. Prior work proposed inner product functional encryption as a technique to build proximity biometric databases (Kim et al., SCN 2018). This is because binary Hamming distance is computable using an inner product. This work identifies and closes two gaps to using inner product encryption for biometric search: Biometrics naturally use long vectors often with thousands of bits. Many inner product encryption schemes generate a random matrix whose dimension scales with vector size and have to invert this matrix. As a result, setup is not feasible on commodity hardware unless we reduce the dimension of the vectors. We explore state of the art techniques to reduce the dimension of the iris biometric and show that all known techniques harm the accuracy of the resulting system. That is, for small vector sizes multiple unrelated biometrics are returned in the search. For length 64 vectors, at a 90% probability of the searched biometric being returned, 10% of stored records are erroneously returned on average. Rather than changing the feature extractor, we introduce a new cryptographic technique that allows one to generate several smaller matrices. For vectors of length 1024 this reduces time to run setup from 23 days to 4 minutes. At this vector length, for the same $90%$ probability of the searched biometric being returned, .02% of stored records are erroneously returned on average. Prior inner product approaches leak distance between the query and all stored records. We refer to these as distance-revealing. We show a natural construction from function hiding, secret-key, predicate, inner product encryption (Shen, Shi, and Waters, TCC 2009). Our construction only leaks access patterns, and which returned records are the same distance from the query. We refer to this scheme as distance-hiding. We implement and benchmark one distance-revealing and one distance-hiding scheme. The distance-revealing scheme can search a small (hundreds) database in 4 minutes while the distance-hiding scheme is not yet practical, requiring 3.5 hours.more » « less
-
Biometric recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using said biological attributes to provide access by verifying the claimed identity of an individual. This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones. Further, the framework promotes innovation by providing interfaces for a plurality of software developers to upload their matching algorithms to the cloud. When a biometric authentication request is submitted, the system uses a criteria to automatically select an appropriate matching algorithm. Every time a particular algorithm is selected, the corresponding developer is rendered a micropayment. This creates an innovative and competitive ecosystem that benefits both software developers and the consumers. As a case study, we have implemented the following: (a) an ocular recognition system using a mobile web interface providing user access to a biometric authentication service, and (b) a Linux-based virtual machine environment used by software developers for algorithm development and submission.more » « less
-
null (Ed.)Accurate segmentation and parameterization of the iris in eye images still remain a significant challenge for achieving robust iris recognition, especially in off‐angle images captured in less constrained environments. While deep learning techniques (i.e. segmentation‐based convolutional neural networks (CNNs)) are increasingly being used to address this problem, there is a significant lack of information about the mechanism of the related distortions affecting the performance of these networks and no comprehensive recognition framework is dedicated, in particular, to off‐angle iris recognition using such modules. In this work, the general effect of different gaze angles on ocular biometrics is discussed, and the findings are then related to the CNN‐based off‐angle iris segmentation results and the subsequent recognition performance. An improvement scheme is also introduced to compensate for some segmentation degradations caused by the off‐angle distortions, and a new gaze‐angle estimation and parameterization module is further proposed to estimate and re‐project (correct) the offangle iris images back to frontal view. Taking benefit of these, several approaches (pipelines) are formulated to configure an end‐to‐end framework for the CNN‐based offangle iris segmentation and recognition. Within the framework of these approaches, a series of experiments is carried out to determine whether (i) improving the segmentation outputs and/or correcting the output iris images before or after the segmentation can compensate for some off‐angle distortions, (ii) a CNN trained on frontal eye images is capable of detecting and extracting the learnt features on the corrected images, or (iii) the generalisation capability of the network can be improved by training it on iris images of different gaze angles. Finally, the recognition performance of the selected approach is compared against some state‐of‐the‐art off‐angle iris recognition algorithms.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3517031.3532521
