Researches Of Face Recognition Based On Sparse Representation And Feature Selection

Face recognition is one of the cutting-edge research tompics in computer vision and pattern recognition areas. Because of its non-touchment and low-expense in system designing, face recognition has been widely applied in security surveillance, human-computer interaction, artificial intelligence, and electronic commerce etc. Targeting on the digital face image recognition, the negative effects that brought by the variety of light and expression, and the cover problems have been studied in the dissertation. By taking the more recent development in machine learning, this dissertation has thoroughly studied the face recognition based on sparse representation and feature selection. The main contributions of this dissertation can be summarized as follows:1) Graph embedding based feature selection.In the graph based feature selection, the stability of the K-nearest graph will degrade with the increasing noise features. In this dissertation, we developed two recursive feature elimination (RFE) methods using feature score (FS) and subset level (SL) score, respectively, for identifying the optimal feature subset. In FS-RFE method, we recursively remove the features with the least feature scores and update the graph with the selected features to reduce the negative influence on graph construction. In SL-RFE, we iteratively calculate the subset level score and recursively remove those feature with least scores based on the updated graph. The experimental results on UMIST and Yalefaces datasets verify that the proposed RFE method can achieve the state-of-art performance compared with the baseline methods such as θG-MFA, θG-LDA,θG-LSDF and SL, and can avoid the negative influence brought by the noise features on the graph effectively.2) Chain sampling methods for feature selection on ultrahigh dimensional problems.Regarding the dimension reduction in extremely high dimensional problems, in this dissertation, a sampling scheme is proposed to enhance the efficiency of recently developed Feature Generating Machines (FGM). In each iteration of FGM, the features are ordered by their scores to form a new feature subset. For high dimensional problems, the entire computational cost of feature ordering will become unbearable. Our method tries to keep those dense features in a buffer, drop those sparse features and speedup the algorithm using chain sampling on instances. In chain sampling, we just keep some features with the largest scores in the buffer when the iteration evolves and exchange the features in the buffer gradually. Finally, we reorder the features in the buffer and find the features with the biggest scores. Our proposed strategy can reduce this computational complexity significantly. Empirical studies on ultrahigh datasets on face image datasets showed the effectiveness of the proposed sampling method.3) Efficient large-scale sparse representation algorithm based on working set.The complexity of sparse representation will sharply increase with the scale of the dictionary. Regarding this issue, an efficient large-scale sparse representation algorithm, named fast decomposed gradient projection algorithm, is proposed in this dissertation for face recognition. In the proposed method, the sparse representation is addressed via solving a box-constrained quadratic programming problem. However, rather than solving the entire large scale problem, the proposed method selects those atoms with the largest absolute gradients as working set, which will transforms the original problem into a series of small box-constrained quadratic problems. By solving these small optimization problems, the large-scale sparse representation can be efficiently solved with very small memory requirements. The efficiency of the large-scale sparse representation can be greatly improved, which can make great improvements on the face recognition accuracy.4) Robust face recognition by sparse representation in wavelet domain.In this paper, we propose a novel robust face recognition algorithm by sparse representation in wavelet domain. Considering that the wavelet transform of an image can preserve its detailed and spatial distribution information it can be employed to extract the facial features. We construct multi-frequency dictionary which contains information of high frequency and low frequency, and obtain the sparse representation of high frencity and low frequency subband. Finally, we have the recognition result by compute the fitting of high frequency and low frequency subband in multi-frequency dictionary. The experimental results over two benchmark face databases demonstrate the robustness and improvements brought by the proposed algorithm.5) Face recognition method based on decision fusion.Most fusion methods on feature level needs to match different types of features. In addtion, because of the information collision of different types of features, the performance of the fusion results may be limited. To address this problem, a decision-level fusion method is proposed in this dissertation for face recognition. Notice that the local binary pattern (LBP) can reflect the local characteristics of the images, while the linear discriminant analysis (LDA) can efficiently extract the global image characteristics. Regarding this fact, we first do Gabor-transform on the images on multiple directions and scales, resulting in Gabor feature presentation of the face image. Then we extract the local features and global features by using the LBP and LDA, respectively. Finally, we fusion the recognition results from K-NN classification in decision level. Experimental results show that the proposed method based on the decision fusion shows superior performance than Gabor-LBP and Gabor-LDA method. More important, the proposed method shows stable performance over the increasing number of testing persons (testing classes).6) Face recognition method based on distributed compressive sensing of near infrared images and visible light images.By assuming that the infrared image and visible light image are sparse with respect to the whole image, we cast the near infrared image and visible light image of the same subject into an ensemble of inter-correlated image. To better capture the information of the two kinds of images to represent the near infrared and visible image of a given subject, we proposed to use the distributed compressive sensing to exploit the aforementioned sparsity of the assembled images. Finally, we proposed to do the image recognition based on the obtained distributed sparse coefficients, which is expected to obtain better performance than that with single near infrared image or visible light image. The experimental results on the benchmark dataset demonstrate the effectiveness of the proposed method.