A Unified Learning Framework for Real Time Face Detection and Classification

This paper presents progress toward an integrated, robust, real-time face detection and demographic analysis system. Faces are detected and extracted using the fast algorithm recently proposed by Viola and Jones [16]. Detected faces are passed to a demographics classifier which uses the same architecture as the face detector. This demographic classifier is extremely fast, and delivers error rates slightly better than the best known classifiers. To counter the unconstrained and noisy sensing environment, demographic information is integrated across time for each individual. Therefore, the final demographic classification combines estimates from many facial detections in order to reduce error rate. The entire system processes 10 frames per second on an 800 MHz Intel PIII.