Integrated approach of multiple face detection for video surveillance
Tae‐Kyun Kim, Sunguk Lee, Jongha Lee, Seok-Cheol Kee, Sang-Ryoung Kim · 2003
For applications such as video surveillance and human computer interfaces, we propose an efficiently integrated method to detect and track faces. Various visual cues are combined with 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 for 640 by 480 pixel images on a Pentium IV 1 GHz.