Face classification using curvature-based multi-scale morphology

In this paper, we present a novel technique for classification of face images that employs Curvature-based Multi-scale Morphology (CMM). Multi-scale Morphology is an image analysis technique that employs mathematical morphology with structuring elements whose spatial dimensions are scaled successively. This "scale-space" representation of images has proven to be an efficient technique for indexing a large database of images. A majority of the existing techniques for multi-scale morphology employ regular and symmetrical structuring elements like cylinders, hemispheres or circular poweroids. The shape of these structuring elements is controlled only by the scaling parameter. In this paper, we propose the use of a structuring element whose shape is a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. A high-dimensional feature vector is obtained by applying the CMM technique to the face images. The dimensionality of the feature vector is reduced by using the PCA technique, and the low-dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance. Experimental results have shown that the proposed CMM technique outperforms existing approaches based on multi-scale morphology.