  In this paper, we present a technique by which high-intensity feature vectors
extracted from the Gabor wavelet transformation of frontal face images, is
combined together with Independent Component Analysis (ICA) for enhanced face
recognition. Firstly, the high-intensity feature vectors are automatically
extracted using the local characteristics of each individual face from the
Gabor transformed images. Then ICA is applied on these locally extracted
high-intensity feature vectors of the facial images to obtain the independent
high intensity feature (IHIF) vectors. These IHIF forms the basis of the work.
Finally, the image classification is done using these IHIF vectors, which are
considered as representatives of the images. The importance behind implementing
ICA along with the high-intensity features of Gabor wavelet transformation is
twofold. On the one hand, selecting peaks of the Gabor transformed face images
exhibit strong characteristics of spatial locality, scale, and orientation
selectivity. Thus these images produce salient local features that are most
suitable for face recognition. On the other hand, as the ICA employs locally
salient features from the high informative facial parts, it reduces redundancy
and represents independent features explicitly. These independent features are
most useful for subsequent facial discrimination and associative recall. The
efficiency of IHIF method is demonstrated by the experiment on frontal facial
images dataset, selected from the FERET, FRAV2D, and the ORL database.
