Research On Feature Extraction Methods For Face Recognition

Feature extraction is a key technique for the face recognition, which directly impact on the performance of the entire system. Among them, linear discriminant analysis (LDA) based on Fisher criteria is the most classic and widely used method, which takes the separability of the patterns as its goal, and seeks an optimal linear transformation by maximizing the ratio of the between-class and within-class scatter matrices. However, as an algebra feature extraction based on statistical techniques, the traditional LDA in the case of small sample size will encounter two practical problems:one arises from the "singularity" of the distribution matrix; the other is due to the estimation error of the distribution matrix.The recent FRVT2006 test results showed that:face recognition performance on still frontal images taken under controlled and cooperative conditions has improved by an order of magnitude since the FRVT 2002, and were more accurate than humans. However, under non-controlled and non-cooperative conditions there is almost an order of magnitude of the decline. There are a lot of non-controlled factors such as change in the illumination, post variation, expression variation, and so on, in which the effects of illumination variation is particularly serious. So, how to extract the robust features against these factors is still a challenging problem.In this thesis, we closely focused on our work to overcome the above-mentioned 3 problems, and to present an effective solution. And the main contributions are summarized as follows:1. Provided an overview of the LDA-based extensionsIn the case of small sample size, the traditional LDA fails due to the singularity of the within-class scatter matrix Sw. Recently, many LDA-based methods have been proposed:the ones overcoming the singularity problem such as Fisherfaces, direct LDA, null space LDA, orthogonal LDA, and etc, and the others reducing the impact of the estimation error of the distribution matrix such as Perturbation LDA, dual-space LDA, three space regularization method, and etc. This thesis described these extensions in detail and presented a certain analysis.2. Theoretical analysis of the characteristics and the relationship among LDA-based extensions dealing with the singularity problemWe carried on the theoretical analysis of the characteristics and the relationship among LDA-based extensions and concluded as follows:GSVD-LDA is equivalent to ULDA; In undersampled cases DLDA nearly can make no use of the null space of Sw and may result in the loss of important discriminative information; DLDA is degenerated as PCA of the between-class scatter with all nonzero principal components if it keeps the complete projection vectors. As a result, DLDA is not an optimal choice for dimensionality reduction from the classification sense.3. Studied on the method reduced the impact of the estimation error of the distribution matrixFrom the matrix Sw's eigenspectrum analytic point of view, Some regularization methods thought that the perturbation caused by the estimation error of the distribution matrix has a large impact on the corresponding eigenspace of the small and zero eigenvalues, then the regularized process for these subspace can reduce the effect of the estimation error, and improves the stability of algorithm. Based on this idea, we adopted the generalized Fisher criteria, and used the total scatter matrix St as an operational object. Then the corresponding eigenspace of non-zero eigenvalues of St is partitioned and used different weighting factor. So the discriminant information inside the eigenspace of the small eigenvalues of St is reserved, and the algorithm stability is improved. The comparison results on PIE face database also showed that our algorithm can take into account the recognition accuracy and computational cost.4. Proposed an approach based on the multi-scale gradient angle and LDA for robust feature extractionAs showed in the results of FERET and FRVT, the recognition performance will suffer from the effect of some factors such as illumination variation, expression variation, pose variation and noise. From the frequency domain point of view, the illumination variation generally reflected in the low-frequency part, and expression variation and noise are mainly distributed in the high-frequency part. The multi-scale gradient angular feature proposed in this thesis possessed two advantages:the localization and multi-resolution of wavelet transform and the illumination invariant of the gradient angle. Using the derivative characteristics of anti-symmetric biorthogonal wavelet, the multi-scale gradient angle can be calculated easily. Combined with the LDA, our algorithm based on the multi-scale gradient angle was more robust and stable. The experimental comparison results on Yale and YaleB showed that:our algorithm can decrease effectively the effect of illumination variation, expression variation and noise, and outperformed the other methods based on illumination invariant feature in the recognition accuracy.