This paper describes an embedded multi-user login system based on fingerprint recognition. The system, built using the Sitsang development board and embedded Linux, implements all fingerprint acquisition, preprocessing, minutia extraction, match, identification, user registration, and template encryption on the board. By careful analysis of the accuracy requirement as well as the arithmetic precision to be used, we optimized the algorithms so that the whole system can work in real-time in the embedded environment based on Intel(R) PXA255 processor. The fingerprint verification, which is the core part of the system, is fully tested on a fingerprint database consists of 1149 fingerprint images. The result shows that we can achieve an accuracy of more than 95%. Field testing of 20 registered users has further proved the reliability of our system. The core part of our system, then embedded fingerprint authentication, can also be applied in many different embedded applications concerning security problems.
This paper presents an experimental study of the implementation of a face recognition system in embedded systems. To investigate the feasibility and practicality of real time face recognition on such systems, a door access control system based on face recognition is built. Due to the limited computation power of embedded device, a semi-automatic scheme for face detection and eye location is proposed to solve these computationally hard problems. It is found that to achieve real time performance, optimization of the core face recognition module is needed. As a result, extensive profiling is done to pinpoint the execution hotspots in the system and optimization are carried out. After careful precision analysis, all slow floating point calculations are replaced with their fixed-point versions. Experimental results show that real time performance can be achieved without significant loss in recognition accuracy.
This paper presents an experimental study of the implementation of a face authentication system for mobile devices. Our system is based on a widely adopted face recognition technique called Principal Component Analysis (PCA). The execution time of the baseline system on a PDA is unacceptably slow -- a typical authentication session takes more than 30 seconds. To make real-time face authentication possible on mobile devices, optimization is needed. In our study, extensive profiling is done to pinpoint the execution hotspots in the system. Based on the profiling results, our optimization strategy focused on replacing the large amount of slow floating point calculations with their fixed-point versions. Range estimation is also carried out to determine the range of floating point values that must be accommodated by the final, fixed-point version of our system. Compared with the baseline system, experimental results indicate that our optimized system runs as much as 47 times faster for PCA projection. Using the optimized system, a complete authentication session takes only 5 seconds. Real time face authentication for mobile device is achieved with no significant loss in recognition accuracy.