Publications

Abstract : The sparse manifold clustering and embedding (SMCE) algorithm can be used to find a low-dimensional face manifold subspace. The neighborhoods are defined automatically and a sparse weight matrix is calculated with non zero values only for the members of the neighborhood. To obtain well separated classes in the low dimensional feature space, we propose to use discriminant functions during the embedding process. Existing manifold techniques do not give accurate results when images of different individuals with varying pose angles, lighting and facial expressions are considered. For these methods a proper choice of the neighborhood used to build the neighborhood graph is important. In SMCE this neighborhood is defined using a sparse optimization technique. K -nearest neighbor (KNN) classifier is used to classify the face images. The proposed method is compared with benchmarks using standard face datasets for varying facial expressions, lighting and poses. Leave-one-out cross validation testing strategy is used to validate the results.