Research On Face Recognition Using Subspace Methods

Automatic recognition of human faces has been an active research area in the communities of pattern recognition, computer vision and information technology due to its immense application potential. A large numbers of methods have been proposed for face recognition in the last several decades. Feature extraction and classification in face recognition are researched in this dissertation. Novel face recognition methods are proposed by using the subspace methods.Image transform is one of the important face feature extraction methods. The multi-scales and multi-directions image features extracted by dual-tree complex wavelet transform (DT-CWT) are shift invariant. Compared with Gabor wavelet, DT-CWT features preserve more information in frequency domain. A new face recognition method is proposed by adopting the DT-CWT and independent component analysis (ICA). The face feature vectors are constructed by the DT-CWT coefficients of the face images. Subspace analysis is applied to feature vectors by using ICA. Feature vectors in ICA subspace is processed by probabilistic reasoning models for classification.Features on horizontal and vertical directions are not included in the 6 fixed directions features which extracted by DT-CWT on each scale. The horizontal and vertical features provide important information for classification because that the key apparatus on human face are distributed on the two directions. Motivated by the deficiency of DT-CWT, a novel face recognition method is introduced by combining the DT-CWT features and the Gabor wavelet features. The face features on horizontal and vertical orientations are extracted by 0°and 90°Gabor wavelet filters. Face feature vectors are conducted by connecting the 0°and 90°Gabor wavelet features and the features extracted by DT-CWT. Subspace analysis is applied to feature vectors by fisherfaces method. Euclidean distance based classifier is exploited to classify the feature vectors in subspace.Bayesian face recognition calculates the intrapersonal and interpersonal variations of the face images. The face variations are modeled by Gaussian probability distribution in the principal component analysis (PCA) subspace. The posteriori probability of the face variations is estimated for classification. However, Gaussian distribution can not describe the face variations accurately due to the complexity of the influence factors for face image. To overcome this deficiency, an ICA based Bayesian face recognition method is presented. Subspace analysis for face variations is applied to feature vectors by exploiting ICA. One dimension probability distributions are estimated separately in the ICA subspace. High dimension distribution is derived by the multiplication of the mutual independent one dimension distributions. The generalized Gaussian distribution is adopted to model the face variations in ICA subspace. The accuracy of the probability distribution estimation and the performance of the face recognition method are improved by the proposed method.