Architectures for Efficient Face Authentication in Embedded Systems Abstract

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 tha