Low resolution and un-sharp facial images are always captured from surveillance videos because of long human-camera
distance and human movements. Previous works addressed this problem by using an active camera to capture close-up
facial images without considering human movements and mechanical delays of the active camera. In this paper, we
proposed a unified framework to capture facial images in video surveillance systems by using one static and active
camera in a cooperative manner. Human faces are first located by a skin-color based real-time face detection algorithm.
A stereo camera model is also employed to approximate human face location and his/her velocity with respect to the
active camera. Given the mechanical delays of the active camera, the position of a target face with a given delay can be
estimated using a Human-Camera Synchronization Model. By controlling the active camera with corresponding amount
of pan, tilt, and zoom, a clear close-up facial image of a moving human can be captured then. We built the proposed
system in an 8.4-meter indoor corridor. Results show that the proposed stereo camera configuration can locate faces with
average error of 3%. In addition, it is capable of capturing facial images of a walking human clearly in first instance in
90% of the test cases.
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.