A Novel Scheme for Generating Secure Face Templates Using BDA
This work addresses the need for secure and accurate face template protection in identity management systems, representing an incremental improvement over existing methods.
The paper tackles the problem of securing face biometric templates by proposing a novel scheme that combines hybrid approaches with binary discriminant analysis, achieving enhanced discriminability and recognition accuracy while providing high security against brute force and smart attacks.
In identity management system, frequently used biometric recognition system needs awareness towards issue of protecting biometric template as far as more reliable solution is apprehensive. In sight of this biometric template protection algorithm should gratify the basic requirements viz. security, discriminability and cancelability. As no single template protection method is capable of satisfying these requirements, a novel scheme for face template generation and protection is proposed. The novel scheme is proposed to provide security and accuracy in new user enrolment and authentication process. This novel scheme takes advantage of both the hybrid approach and the binary discriminant analysis algorithm. This algorithm is designed on the basis of random projection, binary discriminant analysis and fuzzy commitment scheme. Publicly available benchmark face databases (FERET, FRGC, CMU-PIE) and other datasets are used for evaluation. The proposed novel scheme enhances the discriminability and recognition accuracy in terms of matching score of the face images for each stage and provides high security against potential attacks namely brute force and smart attacks. In this paper, we discuss results viz. averages matching score, computation time and security for hybrid approach and novel approach.