Local descriptor margin projections (LDMP) for face recognition
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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.
KeywordsFace recognition Local descriptor Central pixel Feature extraction
This work is supported by the National Natural Science Fund of China (Grant Nos. 61503195, 61462064, 61203243,61402231, 61603192 and 61272077), the Natural Science Fund of Jiangsu Province (Grant No. BK20161580), the University Natural Science Fund of Jiangsu Province of China (Grant No. 15KJB520018, 16KJB520020 and 12KJA63001), the Project Funded by China Postdoctoral Science Foundation (Grant No. 2016M600433), the Project Funded by PAPD and CICAEET, and the Project supported by the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) (Grant No. 30916014107).
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