Face Detection System Using Context-driven Multiple Bayesian Classifier

Face detection is a new development trend and important application in the field of pattern recognition. Face detection and face recognition have a much widely and valued prospect of application in security recognition, personal identification and the work of inspection in public security organs. Face detection is the first stage of face recognition. The researchers always try to find a face location algorithm with high accuracy and efficiency for a long time. Presently, face detection has two kinds of method. One is based on the heuristic modeling method, and the other is based on statistical modeling method. The statistical modeling based detection method is widely used in recent years.The face recognition is mentioned when study the face detection. The main process of face recognition is: detect if there is a face or not in a face image inputted or captured by camera. If it is, give a position, size and information of each face for a further study. Depending on this information, extract the personal feature of identification. And finally, compare the features with these in face database for identification.The issue of face detection is a subset of face recognition, also the premises of face recognition. In the paper, we introduce the different methods of face detection and recognition. The proposed context-driven based multiple Bayesian classifiers scheme is described. And a series of experiments which employ the proposed method to some different application prove the performance of this method.