LBP edge-mapped descriptor using MGM interest points for face recognition

In recent years, face recognition has become a popular topic in academia and industry. Current local methods such as the local binary pattern (LBP), and scale invariant feature transform (SIFT) perform better than holistic methods, but their high complexity levels limit their application. In addition, SIFT-based schemes are sensitive to illumination variation. We propose an LBP edge-mapped descriptor that uses maxima of gradient magnitude (MGM) points. It can completely illustrate facial contours and has low computational complexity. Under variable lighting, experimental results show that our proposed method has a 16.5% higher recognition rate and requires 9.06 times less execution time than SIFT in the FERET database subset fc. In addition, when applied to the Extended Yale Face Database B, our method outperformed SIFT-based approaches as well as saving about 70.9% in execution time. Furthermore, in uncontrolled conditions, our method has a 0.82% higher recognition rate than local derivative pattern histogram sequences (LDPHS) in the Unconstrained Facial Images (UFI) database.