Face Detection System Based On Multi-Stage Classifier

Face recognition is one of the most important topics of the pattern recognition domain, which is of high value for both academic research and application. Now the common recognition algorithms can be divided into several basic categories:face recognition based on geometry, face recognition based on subspace analysis, face recognition based on template matching, face recognition based on Hidden Markov Model, face recognition based on neural network and based on 3D.Nowadays face detection based on Adaboost technology is already very mature. It is difficult to establish a classifier with high performance just via machine learning. But via "Adaboost" technology, it becomes easy by collecting the good "common parts" from a series of the classifier with poor performance. First, the information of face is described via a collection of geometrical feature vectors, and then classifiers are designed to identify the face, at last, the "strong" classifier is established via combination of a series of "weak" ones.This paper proposes a new method of rapid detection based on facial image feature via deeping into several key technologies of multi-stage face detection and recognition system including human face detection,feature extraction,face recognition, Data storage, multi-classifier training:Figure rectangular characteristics of integral feature vector, to build a crude sorting device, use this classifier to detect objects coarse screening Election, so that the background of a large number of parts may cause interference to be filtered out, to speed up the processing speed, to avoid non-human face, part of the sample collection and so on. Experimental results show that the proposed scheme is feasible and effective which can recognize faces quickly and efficiently.