Enhancing face matching in a suitable binary environment

Computerized human face recognition is a complex task of deformable pattern recognition. The principal source of complexities lies in the significant inter-class overlapping of faces due to the variations caused by different poses, illuminations, and expressions (PIE). The popularly used computerized face recognition algorithms like PCA, EBGM etc. are fairly reliable to determine facial attributes from an image. But, in most of the cases the features are extracted in terms of gray textures. When the database size is tuned to millions, then huge processing time is required, as each of the pixel must be represented using at least eight bits. In the present paper, our objective is to minimize the processing time by reducing the number of bits to represent each pixel. This we have done by combining two methods. The first one is a neuro-visually inspired method of figure-ground segregation (NFGS) which can convert the entire face image into a binary 2D array, efficiently. The second one is the scale invariant feature transform (SIFT) which extracts the scale invariant and rotation invariant features from the binarized face image and thereafter matches the features. The proposed algorithm is found successful in actually enhancing the performance of face matching. Psycho-visual experiments also corroborate the fact.