Eigenface or principal component analysis (PCA) methods have demonstrated their success in face recognition, detection, and tracking. The representation in PCA is based on the second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Higher order statistics (HOS) have been used as a more informative low dimensional representation than PCA for face and vehicle detection. We investigate a generalization of PCA, kernel principal component analysis (kernel PCA), for learning low dimensional representations in the context of face recognition. In contrast to HOS, kernel PCA computes the higher order statistics without the combinatorial explosion of time and memory complexity. While PCA aims to find a second order correlation of patterns, kernel PCA provides a replacement which takes into account higher order correlations. We compare the recognition results using kernel methods with eigenface methods on two benchmarks. Empirical results show that kernel PCA outperforms the eigenface method in face recognition.
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