BP Neural Network-based Face Detection

In the recent years, the subject of face detection which was originally put forward as location part has been placed a lot of importance as an independent subject by researchers due to the practical value in the fields of safe access control, vision inspection, content-based retrieval and new generation human-machine interface. Neural Network Algorithm which hides the statistical character of face pattern in its structure and parameter has shown its special advantage in the detection pattern which is complicated and difficult to describe such as faces.Three-lay Error Back Propagation Network was designed with 625 nodes in the input lay, 2 nodes in the output lay and 20 nodes in the hidden lay based on the research on the principle of Neural Network and the character of faces and has been improved in the applicability by the training of a large amount of various face examples and non-faced examples which are more representative after the betterment of collecting method. Now, the step problem of Error Back Propagation Algorithm has been effectively solved by the use of learning rate in the training.Before face detection, the veracity of detection is increased by pretreatment of the detection images and the detection faces with different size and position has been solved by the method of pyramid sub-sampling in the process of window scan.The result of experiment shows that the face detection based on Error Back Propagation Neural Network has achieved a high precision detection rate and a low error alarming rate and can effectively detect the images with multi-face, various size, different orientation and pose, various facial expression under various lighting conditions and its detection capability can be improved by increase the amount and types of training examples.