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Face Recognition and Retrieval Using LLBP and URFB

Face recognition is one of the major issues in biometric technology. It identifies and/or verifies a person by using a 2D/3D physical characteristics of the face images. Several techniques have been proposed for solving a major problem in face recognition such as fisher face, elastic bunch graph matching and support vector machine. However there are still many challenge problems in face recognition system such as facial expressions, pose variations occlusion and illumination change. Those variations dramatically degrade the performance of face recognition system. It is essential to build an efficient system for face recognition. We introduce a novel face representation method for face recognition integrated with URFB called Local Line Binary Pattern (LLBP) summarizes the local spatial structure of an image by thresholding the local window with binary weight and introduce the decimal number as a texture presentation .Moreover it consumes less computational cost. The basic idea of LLBP is to first obtain the binary code both along the horizontal and vertical directions separately and its magnitude, which characterizes the change in image intensity such as edges and corners, is then computed along with the unified relevance feedback(URFB) shows the advantage over traditional retrieval mechanisms. To seamlessly combine texture feature based retrieval system, a query concept-dependent fusion strategy is automatically learned. Experimental results on ORL data base consisting of 400 images show that the proposed framework is widely scalable, and effective for recognition, classification and retrieval.