Partial face recognition by template matching

One of the major difficulties encountered by current face recognition systems deception in the problems of handling partial face recognition such as varying poses, illumination, light scattering, diffused faces i.e., recognition of faces in random in-depth rotations. The face image differences caused by rotations are often superior to the inter-person differences used in distinctive identities. Initially we have taken full face as training set, part of the images are make it as rotational, flip horizontal, flip vertical, scaled up, scaled down facial images which are considered as testing set (for both we use AR database) then convert the colored images in to gray level images. Secondly normalize the histogram of an image it represents the relative frequency of occurrence of the various gray levels in the image. Subsequently, Testing cases are divided in to four ratio cases like as 70:30, 60:40, 50:50, 80:20 using this bifurcation cases of images we will get different recognition results. Simultaneously we have computed histogram of the normalized images and we got features in the form of matrix or vector representation and then compare the values of different testing cases. Finally, matching the features using template matching. The proposed algorithm has been testing with AR dataset and results obtained are satisfactory.