Local descriptor margin projections (LDMP) for face recognition

Feature extraction is a key problem in face recognition systems. This paper tackles this problem by combining the strength of image descriptor with dimensionality reduction technology. So, this paper proposes a new efficient face recognition method-local descriptor margin projections (LDMP). Firstly, we propose a novel local descriptor for face image representation. At this step, an effective and simple metric approach named gray value accumulating distance (GAD) is firstly proposed. And then a novel local descriptor based on GAD is presented to capture the local structure information between central pixel and its neighbors effectively. Secondly, we propose a dimensionality reduction algorithm named maximum margin learning projections (MMLP) which can obtain the low-dimensional and discriminative feature. Finally, experimental results on the Yale, Extended Yale B, PIE, AR and LFW face databases show the effectiveness of the proposed method.