Face recognition with Symmetric Local Graph Structure (SLGS)

• Published in: Expert systems with applications 2014-10, Vol.41 (14), p.6131-6137
• Main Authors: Abdullah, Mohd Fikri Azli, Sayeed, Md Shohel, Sonai Muthu, Kalaiarasi, Bashier, Housam Khalifa, Azman, Afizan, Ibrahim, Siti Zainab
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Biometric; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Computer vision; Exact sciences and technology; Expert systems; Face recognition; Facial; Graphs; Illumination; Local descriptor; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Pixels; Software; Symmetric Local Graph Structure (SLGS); Texture-based
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.04.006

•Proposed a novel method for face recognition using graph-based local features.•Experimented with well-known online face database that is AT&T face database.•Result clearly shows that the proposed method outperforms the control methods.•The performance is evaluated using recognition rate, accuracy, FAR, and FRR. 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.