Research And Implement Of Face Detection And Recognition

Face detection and recognition technique is one of the most challenging subjects in the fields of machine vision and pattern recognition. It is a comprehensive topic which involves in the pattern recognition, the computer vision, the understanding of natural language, image processing and so on. Along with the development of society and the advancement of technology, the actual needs for convenient, reliable and automatic identification are increasingly urgent, and face recognition technology is also a hot topic in research currently.In this thesis face detection and techniques are studied, and a system prototype is designed. The main work is as follows:(1)Adaboost algorithm which is a rapid face detection method is studied in detail, the rectangular features and the method of calculating Integral Image are analyzed. And then the expansion of the rectangular features proposed by Rainer Lienhart is added. Some weak classifiers are constructed using these rectangular features, and then a strong classifier is constructed by cascading the weak classifiers. Using the classifier, a subsystem of face detection is designed. Through the test using a camera, it can be seen that this system is very quickly. At the same time, the face area is processed through gray adjust and histogram equalization, to make the face area is more suitable suited for face recognition.(2)Eigenface based on Principal Component Analysis is analyzed, which is a classic algorithm for face recognition. Eigenfaces are achieved by transforming the images, and all the face pictures can be expressed by linear combination of these several eigenfaces. And the classification methods of human faces and specific measuring function are analyzed in detail. Based on the above, a face recognition subsystem is realized inthis thesis and a experiment was carried on the ORL.(3)The face detection subsystem and recognition subsystem are combined together, to form an automatic face detection and recognition system which is a relatively complete prototype. Under actual test, this system can carry out 5 pictures of detection and the recognition, and then the 5 results are carried on statistical analysis, to obtain the final recognition result. And it has achieved quick speed and better recognition rate.