English

Abstract

Subspace analysis such as the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are widely used feature extraction methods for face recognition. However, since most of them employ holistic basis, local information can not be represented in the subspace. Therefore, in general, they cannot cope with the occlusion problem in face recognition. In this paper, we propose a new method that uses the two-dimensional principal component analysis (2D PCA) for occlusion invariant face recognition. In contrast to 1D PCA, 2D PCA projects a 2D image directly onto the 2D PCA subspace, and each row of the resulting feature matrix exhibits the distribution of corresponding row of the image. Therefore by classifying each row of the feature matrix independently, we can easily identify the locally occluded parts in a face image. The proposed occlusion invariant face recognition algorithm consists of two parts: occlusion detection and partial matching. To detect occluded regions, we apply a novel combined k-NN and 1-NN classifier to each row of the feature matrix of the test face. And for partial matching, similarity between feature matrices is evaluated after removing the rows identified as the occluded parts. Experimental results on AR face database demonstrate that the proposed algorithm outperforms other existing approaches.