Based On A Static Image Of The Human Face Detection And Recognition System Design And Implementation

With the development of information technology and wide application, Face Recognition technology appear to be more and more important such as in computer vision, pattern recognition, artificial intelligence and multimedia technology. How can improve the visual field of pattern recognition efficiency of various algorithms, as well as how to improve the reliability of these algorithms has become the most important research in these areas. Human face detection and recognition research is how to improve the face detection and recognition algorithms efficiency and the effective combination of multiple algorithms, making detection rate and recognition rate. In static image , face detection and recognition are currently being used by a single algorithm, such as color algorithms and Aadaboost Algorithm. With the development of computer hardware, making the effective integration of two algorithms may be applied applications. Face detection and recognition technology and embedded technology applications will be the focus of subsequent years of study.The focus of this thesis is on the PC platform, a static image-based face detection and recognition systems design and implementation, at the same time ported to an embedded platform ready.In my post-graduate stage , I read a lot of references on human face detection and recognition, In this paper, I have done a summary description on human face detection and recognition of the emergence, development, current status and future trends . Human face detection and recognition of color algorithms and Adaboost algorithm a comprehensive exposition. I simple description and presentation of the software system. The software features and applications areas have been summarized . In theory, software used this color combination of algorithm and Adaboost algorithm, taking into account the efficiency of the algorithm, the two kinds of algorithms has been improved, allowing better detection and identification. In summing up, based on these theories, this paper face detection and identification system for the overall design . First read the color static images , second use color algorithm probably the face of regional identity , last use Adaboost algorithm for accurate detection of classifier. The use of color space conversion method for color space conversion, set the chrominance components and luminance component as far as possible independent of each other has no effect. Use mathematical morphology method of expansion and corrosion over the face region processing. Use the classifier of Opencv to detect. Finally on the human face using HMM training and recognition...