Multiple Face Detection for Video Surveillance

Sung Uk Lee


Abstract

For applications such as video surveillance and human computer interface, we propose an efficiently integrated method to detect and track faces. Various visual cues are combined to the algorithm: motion, skin color, global appearance and facial pattern detection.

The ICA (Independent Component Analysis)-SVM (Support Vector Machine) based pattern detection is performed on the candidate region extracted by motion, color and global appearance information.

Simultaneous execution of detection and short-term tracking also increases the rate and accuracy of detection. Experimental results show that our detection rate is 91% with very few false alarms running at about 4 frames per second on 640 by 480 pixel images for a Pentium IV 1GHz.


Maintained by Philippos Mordohai