Learning the human face concept in black and white images
Presents a learning approach for the face detection problem. The problem can be stated as follows: given an arbitrary black and white, still image, find the location and size of every human face it contains. Numerous applications of automatic face detection have attracted considerable interest in this problem, but no present face detection system is completely satisfactory from the point of view of detection rate, false alarm rate and detection time. We describe an inductive learning-based detection method that produces a maximally specific hypothesis consistent with the training data. Three different sets of features were considered for defining the concept of a human face. The performance achieved is as follows: 85% detection rate, a false alarm rate of 0.04% of the number of windows analyzed and 1 minute detection table for a 320/spl times/240 image on a Sun Ultrasparc 1.