Constructing Descriptive and Discriminant Features for Face Classification




Yu, Jie
Tian, Qi

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UTSA Department of Computer Science


Linear Discriminant Analysis (LDA) has been widely applied in the field of face classification because of its simplicity and efficiency in capturing the most discriminant features. However LDA often fails when facing the small sample set and change in illumination, pose or expression. To overcome those difficulties, Principal Component Analysis (PCA), which recovers the most descriptive/informative features in the dimension-reduced feature space, is often used in the preprocessing stage. Although there is a trend of preferring LDA to PCA in classification, it has been found that PCA may perform better than LDA in some cases, especially when the size of the training set is small. In this paper we propose a parametric framework that can unify PCA and LDA to find both discriminant and descriptive features. To avoid the exhaustive parameter searching, we incorporate a non-linear boosting process to enhance a pool of hybrid classifiers and adaptively combine them into a more accurate one. To evaluate the performance of our boosted hybrid method, we compare it to state-of-the-art LDA variants and the other PCA-LDA techniques on three widely used face image benchmark databases. The experiment results show the superior performance of our novel boosted hybrid discriminant analysis.





Computer Science