A DSP-based approach for the implementation of face recognition algorithms

Face recognition is an important part of today’s emerging biometrics and video surveillance markets. Recent years have witnessed an exploding interest in the development of face recognition algorithms and products. Currently, face recognition systems are usually implemented on general purpose processors. As face recognition algorithms move from research labs to the real world, power consumption and cost become critical issues. This motivates searching for implementations using a digital signal processor (DSP). Our goal in this paper is to explore the feasibility of implementing DSP-based face recognition systems. To achieve this goal, we implement a fully automatic face recognition system on Texas Instruments’ TMS320C6416 DSP, profile performance, and analyze opportunities for optimization. The results of our experiments demonstrate that a generic C implementation with a modest C level optimization effort results in a face recognition software prototype that has low CPU and memory requirements. Hence, it appears that well-optimized face recognition implementations on DSPs can be an effective choice for embedded face recognition products.