Improving the Recognition of Occluded Faces by Means of Two-dimensional Orthogonal Projection into Local Subspaces

The paper presents a problem of reducing the influence of natural occlusion on face recognition accuracy. It is based on transformation (two-dimensional Karhunen-Loeve Transform) of face parts into local subspaces calculated by means of two-dimensional Principal Component Analysis and two-dimensional Linear Discriminant Analysis. We use a sequence of operations consisting of face scale and orientation normalization and individual facial regions extraction. Independent recognitions are performed on extracted facial regions and their results are combined in order to perform a final classification. The experiments on images taken from publicly available datasets show that such a simple algorithm is able to successfully recognize faces without high computational overhead, in contrast to more sophisticated methods presented recently. In comparison to typical, whole-face-based approach, developed method gives significantly better accuracy.