Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, access control, smart cards: a not so simple combination
Proceedings of the fourth working conference on smart card research and advanced applications on Smart card research and advanced applications
Biometrics: Identity Verification in a Networked World
Biometrics: Identity Verification in a Networked World
An Analysis of Minutiae Matching Strength
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Biometric Recognition: Security and Privacy Concerns
IEEE Security and Privacy
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Guide to Biometrics
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Linear Representations of Images for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing security and privacy in biometrics-based authentication systems
IBM Systems Journal - End-to-end security
Handbook of Face Recognition
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Distance Measures for Face-Recognition Supporting Revocable Biometric Tokens.
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
The effectiveness of generative attacks on an online handwriting biometric
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Vulnerabilities in biometric encryption systems
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
On the vulnerability of face verification systems to hill-climbing attacks
Pattern Recognition
An evaluation of indirect attacks and countermeasures in fingerprint verification systems
Pattern Recognition Letters
Gaze pattern lock for elders and disabled
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
Computer Vision and Image Understanding
Hi-index | 0.14 |
Regeneration of templates from match scores has security and privacy implications related to any biometric authentication system. We propose a novel paradigm to reconstruct face templates from match scores using a linear approach. It proceeds by first modeling the behavior of the given face recognition algorithm by an affine transformation. The goal of the modeling is to approximate the distances computed by a face recognition algorithm between two faces by distances between points, representing these faces, in an affine space. Given this space, templates from an independent image set (break-in) are matched only once with the enrolled template of the targeted subject and match scores are recorded. These scores are then used to embed the targeted subject in the approximating affine (non-orthogonal) space. Given the coordinates of the targeted subject in the affine space, the original template of the targeted subject is reconstructed using the inverse of the affine transformation. We demonstrate our ideas using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA) with Mahalanobis cosine distance measure, Bayesian intra-extrapersonal classifier (BIC), and a feature-based commercial algorithm. To demonstrate the independence of the break-in set with the gallery set, we select face templates from two different databases: Face Recognition Grand Challenge (FRGC) and Facial Recognition Technology (FERET) Database (FERET). With an operational point set at 1% False Acceptance Rate (FAR) and 99% True Acceptance Rate (TAR) for 1196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve a 73% chance of breaking in as a randomly chosen target subject for the commercial face recognition system. With similar operational set up, we achieve a 72% and 100% chance of breaking in for the Bayesian and PCA based face recognition systems, respectively. With three different levels of score quantization, we achieve 69%, 68% and 49% probability of break-in, indicating the robustness of our proposed scheme to score quantization. We also show that the proposed reconstruction scheme has 47% more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts. Given that the proposed template reconstruction method uses distinct face templates to reconstruct faces, this work exposes a more severe form of vulnerability than a hill climbing kind of attack where incrementally different versions of the same face are used. Also, the ability of the proposed approach to reconstruct actual face templates of the users increases privacy concerns in biometric systems.