The Human Detection Based On Face Characters

With the development of computer vision, the traditional identity verifying methods have not been satisfied people's demand. Also, iris and fingerprint detections rely on devices, so these can not be used in large scope.Face recognition is a more effective and friendly method which does not rely on traditional devices such as keyboards, mice, and displays for human computer interaction. This field correlates some other knowledge such as identification, digital image processing, and psychology and et al. Therefore, face recognition has been object of much interest in the recent years. It has many applications in a variety of fields such as identification for low enforcement, authentication for banking and security system access, and personal identification among others. Furthermore, to all these applications, there is an increasing interest to specify standardized descriptions of various types of multimedia information. This description will be associated with the content itself, to allow fast and efficient searching for material that is of interest to the user. Face detection is very important in the face recognition system.The definition of face detection is: Given an image, the goal of face detection is to determine whether or not there are any faces in the image, and if present, return the image location and extent of each face. But, pose estimation, illumination, facial expression and so on impact the location.The objective of this work is the integration and optimization of an automatic face detection tracking system. Firstly this paper gives a comprehensive and detailed introduction on the face detection technology and an analysis on the recent advances in the field. And then, in the research work, varied skin color segmentation is done in the complex backgrounds. In the paper, color segmentation is made in YCrCb color space. It extract features from the quantized of Cr or Cb. After that, it locate the face rely on area and scale.In addition, the second stage is based on the Principal Components Analysis (PCA) approach which has been modified to cope with the video indexing application. In the stage, face and non-face classifier are based on faces training and efficient non-faces training. After the two stages, several improvements are proposed. Good results have been obtained.