Variants of Locality Preserving Projection for Modular Face and Facial Expression Recognition

Abstract

Locality Preserving Projection (LPP) is one of the widely used approaches for finding intrinsic dimensionality of high dimensional data by preserving the local structure. Data points which are neighbors but belong to different classes are thereby projected as neighbors in the projection space, causing problem of discrimination. Various extensions of LPP have been proposed to enhance the discrimination power achieve better between class separation. In case of face recognition using full face images, if any portion of the face image is distorted, it may reflect on the recognition performance. Humans have the capability to recognize faces even by looking at some parts of the face. This article is an attempt to replicate the same on machines by only considering some of the informative regions of the face. Instead of the entire image, variants of LPP are applied on parts of face images and recognition is performed by combining the results of their reduced dimensional representations. Face and facial expression recognition experiments have been performed on some of the benchmark face databases.