Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
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This paper presents a framework to track multiple persons in realtime. First, a method with real-time and adaptable capability is proposed to extract face-like regions based on skin, motion, and silhouette features. Then, a two-stage face verification algorithm is proposed to quickly eliminate false faces based on the face geometries and the Support Vector Machine(SVM). In order to overcome the effect of lighting changes, a method of color constancy compensation is applied. Then, a robust tracking scheme is applied to track multiple persons based on a face-status table. With the table, the system has extreme capabilities to track different persons at different statuses, which is quite important in face-related applications. Experimental results show that the proposed method is much robust and powerful than other traditional methods.