Architectures for efficient face authentication in embedded systems

Biometrics represent a promising approach for reliable and secure user authentication. However, they have not yet been widely adopted in embedded systems, particularly in resource-constrained devices such as cell phones and personal digital assistants (PDAs). In this paper, we investigate the challenges involved in using face-based biometrics for authenticating a user to an embedded system. To enable high authentication accuracy, we consider robust face verifiers based on principal component analysis/linear discriminant analysis (PCA-LDA) algorithms and Bayesian classifiers, and their combined use (multi-modal biometrics). Since embedded systems are severely constrained in their processing capabilities, algorithms that provide sufficient accuracy tend to be computationally expensive, leading to unacceptable authentication times. On the other hand, achieving acceptable performance often comes at the cost of degradation in the quality of results. Our work aims at developing embedded processing architectures that improve face verification speed with minimal hardware requirements, and without any compromise in verification accuracy. We analyze the computational characteristics of face verifiers when running on an embedded processor, and systematically identify opportunities for accelerating their execution. We then present a range of targeted hardware and software enhancements that include the use of fixed-point arithmetic, various code optimizations, application-specific custom instructions and co-processors, and parallel processing capabilities in multi-processor systems-on-chip (SoCs). We evaluated the proposed architectures in the context of open-source face verification algorithms running on a commercial embedded processor (xtensa from Tensilica). Our work shows that fast, in-system verification is possible even in the context of many resource-constrained embedded systems. We also demonstrate that high authentication accuracy can be achieved with minimum hardware overheads, while requiring no modifications to the core face verification algorithms.