Independent component analysis in a local facial residue space for face recognition

In this paper, we propose an Independent Component Analysis (ICA) based face recognition algorithm, which is robust to illumination andpose variation. Generally, it is well known that the 5rst few eigenfaces represent illumination variation rather than identity. Most Principal Component Analysis (PCA) based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the “residual face space”. We found that ICA in the residual face space provides more e9cient encoding in terms of redundancy reduction androbustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separatedinto several facial components, local spaces, andeach local space is representedby the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple andfacilitate classi5cation by a linear encoding. Various experimental results show that the accuracy of face recognition is signi5cantly improvedby the proposedmethodunder large illumination andpose variations.

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