  Human face recognition is, indeed, a challenging task, especially under the
illumination and pose variations. 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 gray level images, under the
environment of illumination and pose variations. Both the algorithms convert
2-D gray level 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
databases,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.
