Research On The Human Face Detection And Recognition

Automatic Face Recognition (AFR) is a technology for person authentication by using the digitized facial features. For the past two decades, it has become one of the most challenging research topics in the field of image processing, pattern recognition and computer vision. Because of its tremendous potential applications in law enforcement, security control, and video surveillance, AFR has attracted more and more attentions from many research institutes and government organizations including departments in charge of defense, security, and information.A Fully Automatic Face Recognition System (FAFRS) consists of functions including face detection from an input image, face image pre-processing, facial feature extraction, and classifier. These problems have been intensively investigated by researchers and many useful algorithms have been developed. This thesis studies the theories and methods of FR (face 'recognition) systematically. The main research work includes:Firstly, the thesis collects and studies lots of papers and reports on human face detection and recognition home and abroad of these years. Several methods of pretreatment to face images are introduced. A method of image preprocessing with better illumination robustness is presented.Secondly, we develop a demo platform based on Adaboost to implement a static detector for detecting face regions from arbitrary images. A method based on edge strength and a fast searching strategy based on wavelet transform is also used to speed up face detection.Finally, in the part of classification and recognition, we discuss the Nearest Neighbor and K-Nearest Neighbor mainly. And improve them in our classify method.