Efficient face recognition algorithms based on transformed shape features

Human face recognition is, indeed, a challenging task, especially under the illumination and pose variations. It is challenging as well as attractive for its usefulness in the area of crime detection and identity verification. We examine in the present paper effectiveness of two simple algorithms using coiflet packet and Radon transforms to recognize human faces from some databases of still graylevel images, under the environment of illumination and pose variations. Both the algorithms convert 2-D graylevel training face images into their respective depth maps or physical shape which are subsequently transformed by Coiflet packet and Radon transforms to compute energy for feature extraction. Experiments show that such transformed shape features are robust to illumination and pose variations. With the features extracted, training classes are optimally separated through linear discriminant analysis (LDA), while classification for test face images is made through a k-NN classifier, based on L₁ norm and Mahalanobis distance measures. Proposed algorithms are then tested on face images that differ in illumination, expression or pose separately, obtained from three data bases, namely, ORL, Yale and Essex-Grimace databases. Results, so obtained, are compared with two different existing algorithms. Performance using Daubechies wavelets is also examined. It is seen that the proposed Coiflet packet and Radon transform based algorithms have significant performance, especially under different illumination conditions and pose variation. Comparison shows the proposed algorithms are superior.