Face recognition with Symmetric Local Graph Structure (SLGS) |
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| Affiliation: | 1. CEFET-RJ, Brazil;2. PROSAICO – PEL/DETEL – UERJ, Brazil;3. PEE/COPPE/DEL/Poli, UFRJ, Brazil;1. Instituto Superior Técnico, Universidade de Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;2. INESC-ID Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;1. Department of Energy, Politecnico di Milano, via Ponzio 34/3, 20133 Milan, Italy;2. Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, Ecole Centrale Paris and Supelec, Paris, 92295 Chatenay-Malabry Cedex, France;3. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK;1. Institute for Development & Research in Banking Technology, Hyderabad, India;2. School of Computer & Information Science, University of Hyderabad, Hyderabad, India;1. SEM, Beijing Jiaotong University, Shangyuancun 3#, Haidian Ward, Beijing 100044, China;2. IPS, Waseda University, 808-0135, Japan |
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| Abstract: | Face recognition demonstrates the significant progress in the research field of biometric and computer vision. The fact is due to the current systems perform well under relatively control environments but tend to suffer when the present of variation in pose, illumination, and facial expression. In this work, a novel approach for face recognition called Symmetric Local Graph Structure (SLGS) is presented based on the Local Graph Structure (LGS). Each pixel is represented with a graph structure of its neighbours’ pixels. The histograms of the SLGS were used for recognition by using the nearest neighbour classifiers that include Euclidean distance, correlation coefficient and chi-square distance measures. AT&T and Yale face databases were used to be experimented with the proposed method. Extensive experiments on the face database clearly showed the superiority of the proposed approach over Local Binary Pattern (LBP) and LGS. The proposed SLGS is robust to variation in term of facial expressions, facial details, and illumination. Due to good performance of SLGS, it is expected that SLGS has a potential for application implementation in computer vision. |
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