Finding human faces automatically in an image is a difcult yet important rst step to a fully automatic face recognition system. This paper presents an examplebased l e arning approach for locating unoccluded f r ontal views of human faces in complex scenes. The technique represents the space of human faces by means of a few view-based \face" and \non-face" pattern prototypes. At each image location, a 2-value distance m e asure i s c omputed b etween the local image pattern and each prototype. A t r ained classi er determines, based on the set of distance m e asurements, whether a human face e x i s t s a t t h e current image location. We show empirically that our distance metric is critical for the success of our system.
Sung and Poggio REF report an example-based learning approach for locating vertical frontal views of human faces.
Example-based learning for view-based human face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Science