Subspace Analysis And Classification Method For Face Recognition

Face recognition,as the most natural and explicit approach of biological feature recognition,has attracted more academic and industrial concentration in recent decades.A central issue to a successful approach for face recognition is how to extract discriminative feature from the facial images.Many feature extraction methods have been proposed and among them the subspace analysis has received extensive attention owing to its appealing properties of efficiency.This dissertation focuses on the subspace analysis and classification methods in case of face recognition.The main contributions of the dissertation can be noted as following:①An algorithm named Discriminative Common Vectors in the PCA transformed space(PCA+DCV) was proposed.Based on the analysis that the optimal projections of DCV can be searched within the more compact subspace,PCA+DCV performs two Gram-Schmidt orthogonalization procedures in the PCA transformed space to obtain the same optimal projection matrix as original DCV algorithm.PCA+DCV is a fast algorithm calculating the discriminative common vectors,which makes the DCV method more feasible to the high dimensional pattern classification such as face recognition.②Furthermore,with the facility offered by PCA analysis procedure in PCA+DCV,we gave a Weighted PCA+DCV algorithm.The algorithm subtly weights the facial components in PCA space while calculating optimal projection matrix,which is potential to enrich the representative information and thus improves DCV's recognition performance.③The dissertation established an unified framework and corresponding converging iterative update algorithm for Nonnegative Matrix Factorization subspace analysis.Under this framework,the basic principle of major subspace analysis such as PCA,Fisher LDA,Local Preserving Projection can be applied subjecting to non-negativity constraints in learning basis.This ensures that the components are combined to form a whole in the non-subtractive way.For this reason,the NMF unified framework yields a series of subspace analysis learning a parts-based representation,which maybe better consistent with the human being intuitive meaning of adding parts to form a whole.④Aiming to face recognition one sample problem,the dissertation proposed the weighted modular 2DPCA(two-dimensional principal component analysis) method.Due to the fact that the PCA's performance can be considered as an upper limit for most of 1D image vector based subspace methods in one sample problem,the new method firstly performs 2DPCA feature extraction for sub-image-blocks.In this way,the scatter matrix of data can be estimated more stably than PCA,as well as local information was retained.Then optical flow method was used to quantitatively estimate the difference of blocks between face images,which is introduced as prior knowledge for enforcing a local-depended classification.The experiment results indicate that,in one sample problem,the weighted modular 2DPCA method is superior to conventional 1D-data-based subspace analysis method in terms of recognition accuracy and robustness.⑤The dissertation developed a novel multi-class support vector classifier (SVC) called MLMC,which considers all classes data in one QP optimization formulation without increasing the size of the problem proportional to the number of classes.MLMC overcomes the computational efficiency limitation for multi-class SVCs of one QP problem in case of large scale samples learning.Compared with the nearest neighbor classifier(NNC) which is widely adopted by subspace method for classification after feature extraction, MLMC could give better decision hyperplanes in the feature space,thus offers a new alternative decision mechanism for the large scale face recognition application.