Abstract
Face recognition is one of popular topics in academic and industrial areas in recent years. Numerous approaches have been developed nowadays, but there are still several challenges in real-world circumstances. Present local methods such as local binary pattern (LBP) [4], [6], local derivative pattern (LDP) [10] and scale invariant feature transform (SIFT) [14] own better performance than holistic methods; however, high complexity results in some limitations for applications such as mobile devices. In addition, SIFT-based schemes are sensitive to illumination variation. Thus we propose a LBP Edge-mapped descriptor by using Maxima of Gradient Magnitude (MGM) [20] points. It is a robust, simple and fast descriptor. LBP Edge-mapped descriptor is a string of binary codes which record surrounding information of illumination and edges of MGM [20]. It can illustrate facial contours completely and have low computational complexity simultaneously. Due to binary codes, a simple matching method can be adopted for face recognition. Under variable lighting, experimental results show that our method has 16.5% higher recognition rate and spends 9.06 times less execution time than SIFT in FERET fc [22]. Besides, our method outperforms SIFT-based approaches and saves about 70.9% execution time compared with SIFT in the Extended Yale Face Database B [32]. In the variation of expression, our method maintains acceptable recognition rate and has 7.50 times less computational time than SIFT in FERET fb [22]. Furthermore, in uncontrolled conditions, our method owns 0.82% higher recognition rate than local derivative pattern histogram sequences (LDPHS) [10] in Unconstrained Facual Images (UFI) Database [30].