A Save-Fail mechanism for face detection validation

A new automated mechanism named save-fail for validating face detection is proposed in this paper in order to determine whether or not there does exist faces in the candidate region which was located by the AdaBoost face detection algorithm. The mechanism is based on one simple fact, every face has two eyes and a nose, and human eyes and nose has specified features that can be used to distinguish them from the other objects; during the validation procedure, the mechanism first detects eyes and nose in the candidate region, then weights the eyes and nose detection results and normalizes these results to the designed range to accumulate the weights, at last, according to the relationship between the accumulated weight and the pre-defined threshold, determine whether or not there does exist faces. The experiments on CMU-MIT face set and practical testing set show that the mechanism is rather effective in the face detection system, to the positive samples, the mechanism greatly decreases the false detection and little correct detection is lost; to the negative samples, the mechanism both greatly decreases the false detection and improves the correction detection.