Face Detection And Recognition Based On Sparsity Methods

Human face embodies extremely rich information and is the key symbol for distinguishing, recognizing and memorizing individuals. It plays an important role in computer vision, pattern recognition and multimedia technology. Therefore, automatic recognition of human face is one of the most challenging subjects in pattern recognition and computer vision. Our work in this thesis relates with two areas of automatic recognition of human face:face detection and recognition. The main work is as follows.Based on the theory of Compressed Sensing (CS), the goal of Sparse Representation Classifier (SRC) is to find a sparse vector which is a linearly optimal representation for a test sample by using all training samples, and use it to classify this test sample. If the samples are distributed in the same direction, SRC can not classify them exactly. To solve this problem, we propose a Kernel Sparse Representation Classifier (KSRC), which introduces the Mercer kernels to SRC. As the similarity measure between samples, RBF kernel function is a good solution to the problem. The experiments on artificial data, UCI database and Extended Yale B database have verified the effectiveness of KSRC.Based on Kernel Sparse Representation Classifier, we present an ensemble method for KSRCs. The random projection used in KSRC and SRC is an effective way for dimensionality reduction. But for different random matrix, KSRC will get different results. So we use the ensemble of KSRCs to ensure the stability of the algorithm. There are many rules to combine the multiple classifiers, such as Max, Min, Sum, Mean, and Majority Vote rules. The experiments have verified the effectiveness of combining classifiers, and also show that Sum and Mean are better ways.Support Vector Machines (SVMs) have been applied to face detection. But the test speed of SVMs is not satisfied. In order to improve the test speed, we apply 1-norm SVMs to face detection.1-norm SVMs adopt the 1-norm regularization which can induce sparsity. It has been shown that the solution of 1-norm SVM is sparser than standard SVM, so use 1-norm SVM to face detection can improve the detection speed. We have verified the effectiveness of reducing the detection time by experiments.