Complex Context Of The Fast Face Detection And Recognition

Face detection and recognition research is related to the fields of pattern recognition, image processing, physiology, cognitive science and so on. At the same time, it is tied with other researches such as Biometric Authentication and Human-Computer Interaction. It has become one of the key issues because of its practical essentiality in many aspects, especially in the field of security.In the thesis, the fundamental theories and the key technologies of fast face detection and recognition from images with complex background are mainly researched. The topics such as face representation, classifier design and the design of Face Detection and Recognition System for images with complex background are discussed in details. Research work includes the following contents.Firstly, different skin models are researched in four different color spaces. The experiments show that Linear Model or Single Gaussian Model in HIS color space can get better results under complex background. With the help of skin models, it can get rid of the complexity of the background, confine the range of valid areas and lay a basis for fast and accurate face detection. However, it is easily affected by illumination. The robustness is improved by considering the K.-L transformation.Secondly, the face representation and its fast algorithms are summarized. There are some research approaches such as PCA, LDA, Gabor and Like-Harr etc to get features, and some stable and efficient facial features are obtained by these means. The method of selecting features by Adaboost is used to discard redundant features. Cascade representation is adopted to represent face rapidly to improve the fast face representation from coarse to fine, from simple to complex, and to improve the speed and accuracy of the processing.Subsequently, designs of the classifiers are described in three aspects. First of all, some common classifiers are firstly discussed such as SF(Sign Function), NN(Nearest Neighbour),Adaboost, SVM(Support Vector Machine), ANN(Artificial Neural Network) etc. Then appropriate classifiers for face detection and recognition are selected. After that, the method of detecting poses of faces by RBF and PCA is proposed. The classifier with ability of feature selection is studied to prepare for face cascade representation and to make it possible to detect and recognize face fast and accurately. Finally, construction of an array of classifiers is researched, and an effective method to design classifiers of fast face detection and recognition with complex background is presented, which is able to radically discard redundant areas and realize a robust real-time face detection designed for complex background and recognition system with large face database.Finally, a fast face detection and recognition system for images with complex background is proposed and implemented by combining face cascade representation and classifier design. The model system accomplishes face detection and recognition from images with complex background robustly and accurately. When applied, the system runs at 6 frames per second in P41.6G.