Study On Four Face Recognition Methods

Face recognition was one of the most prominent areas in pattern recognition for several decades. Numerous face recognition methods have been proposed. Current systems can do fairly accurate recognition under constrained scenarios using these face recognition methods. It has attracted much attention due to its potential application values. Nowadays, face recognition has great value in public safety, intelligence surveillance, identification, E-commerce, multimedia, digital entertainment and so on.Requirements of the practicality of the face recognition are increased along with the wide use. Also, most of the current face recognition methods can do fairly accurate recognition in fixed environment. But the fixed environment constrain is not suitable for most of the application. This paper discuss the problems mentioned above, the following contributions are made:1. In order to overcome the shortcomings of Principal Component Analysis(PCA) and bionic methods in feature extracting and dimension reduction, a method for extracting Gabor features of face images based on Gabor wavelet is presented. First, Gabor features are extracted from face images. After reduced dimension by 2DPCA, the nearest classifier is trained for classification. The experiments being performed on Yale Face Database B and PIE human face image database show the method presented in this paper is superior to bionic recognition algorithm and PCA algorithm.2. According to the difference between face recognition algorithm in Euclidean metric subspace and human visual perception system, we propose a face recognition method in correlation measure subspace. It aims to find a subspace with preserving correlation measure by correlation multidimensional scaling. Then high-dimensional face image data are projected into such a space. As a result, the similarities between face images are preserved. Since there is a low-dimensional nonlinear manifold lying in the high-dimensional data, the dimensionality and nonlinear geometry of that manifold often is embedded in the similarities between the data points. Thus, the proposed method can effectively learn the nonlinear manifold embedded in the high-dimensional face images. The experimental results on various databases show that the proposed methods can effectively extract the intrinsic structure of high-dimensional data.3. From the way of human cognition, we propose a local matching face recognition method based statistical learning. It first divides the image into several subimages, then each subimage is considered as a weak classifier. The Adaboost learning algorithm is used to train the weak classifiers and construct a strong classifier. As a result, each subimage is effectively combined together to explore their best discriminanting power and improve the classification accuracy. Compared with holistic matching methods, the local matching method is robust to variations in illumination, expression, and pose, etc. The experimental results show the proposed method can improve the face recognition accuracy and is robust to variations in illumination and expression.4 Sparse representation is difficult to explain the physical meanings of negative coefficient and can not be solved by using gradient descent method. According the limitation of sparse representation, we propose a face recognition method via nonnegative sparse representation. In this method, the testing image is represented as a linear combination with nonnegative coefficients of the training images. The nonnegative limitation is more consistent with human cognitive and has practical physical interpretation. The proposed method transforms L1 norm minimization into L2 norm minimization which can be solved by using gradient descent method. This method achieves state-of-the-art performance using raw imagery data, with no need for dimension reduction, feature selection, synthetic training examples or domain-specific information. Extensive experiments on Extended Yale B database verify the efficacy of the proposed method.