  This paper proposes a technique for automatic face recognition using
integrated multiple feature sets extracted from the significant blocks of a
gradient image. We discuss about the use of novel morphological, local
directional pattern (LDP) and gray-level co-occurrence matrix GLCM based
feature extraction technique to recognize human faces. Firstly, the new
morphological features i.e., features based on number of runs of pixels in four
directions (N,NE,E,NW) are extracted, together with the GLCM based statistical
features and LDP features that are less sensitive to the noise and
non-monotonic illumination changes, are extracted from the significant blocks
of the gradient image. Then these features are concatenated together. We
integrate the above mentioned methods to take full advantage of the three
approaches. Extraction of the significant blocks from the absolute gradient
image and hence from the original image to extract pertinent information with
the idea of dimension reduction forms the basis of the work. The efficiency of
our method is demonstrated by the experiment on 1100 images from the FRAV2D
face database, 2200 images from the FERET database, where the images vary in
pose, expression, illumination and scale and 400 images from the ORL face
database, where the images slightly vary in pose. Our method has shown 90.3%,
93% and 98.75% recognition accuracy for the FRAV2D, FERET and the ORL database
respectively.
