Integrated shape and intensity coding scheme for face recognition

This paper introduces a new face coding scheme which employs an Enhanced Fisher Classifier (EFC) operating on integrated shape and intensity features. The dimensionalities of the shape and the intensity image spaces are first reduced using Principal Component Analysis (PCA), constrained by the EFC for enhanced generalization. The reduced shape and the intensity features are then integrated through a normalization procedure to form integrated features. Experiments using 600 face images from the FERET database of varying illumination and corresponding to 200 subjects, whose facial expression can vary, show the feasibility of the new face coding scheme. In particular, the EFC achieves 98.5% recognition rate using only 25 features. Our experiments also show that the integrated shape and intensity features carry the most discriminating information followed in order by textures, shape vectors, masked images, and shape images.