DocumentCode :
2466494
Title :
Gradient feature matching for expression invariant face recognition using single reference image
Author :
Alex, Ann Theja ; Asari, Vijayan K. ; Mathew, Alex
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Dayton, Dayton, OH, USA
fYear :
2012
fDate :
14-17 Oct. 2012
Firstpage :
851
Lastpage :
856
Abstract :
Automatic recognition of human faces irrespective of the expression variations is a challenging problem. In this paper, we propose a novel method for face recognition based on `edge-strings´. Experimental studies on face perception have shown the significance of edge features in visual perception and learning. In the proposed technique, the edges of a face are identified, and a feature string is created from edge pixels. This forms a symbolic descriptor corresponding to the edge image referred to as `edge-string´. The `edge-strings´ are then compared using the Smith-Waterman algorithm to match them. The class corresponding to each image is identified based on the number of string primitives that match. Local string alignment algorithm is more robust to noise than global alignment algorithm; it gives better performance even if the input image is noisy. In addition, this method needs only a single training image per class. The proposed technique is a good solution for expression invariant face recognition. The effectiveness of the proposed method is compared with state-of-the-art algorithms on the Yale Face database, the Japanese Female Face Expression database (JAFFE) and CMU AMP Face EXpression database.
Keywords :
edge detection; emotion recognition; face recognition; image matching; CMU AMP face expression database; Japanese female face expression database; Smith-Waterman algorithm; Yale Face database; automatic human face recognition; edge feature; edge-string; expression invariant face recognition; expression variation; face perception; gradient feature matching; local string alignment algorithm; single reference image; string primitive; symbolic descriptor; visual learning; visual perception; Databases; Face; Face recognition; Feature extraction; Heuristic algorithms; Image edge detection; Training; Expression Invariant Face Recognition; Gradient Features; String Alignment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-1713-9
Electronic_ISBN :
978-1-4673-1712-2
Type :
conf
DOI :
10.1109/ICSMC.2012.6377834
Filename :
6377834
Link To Document :
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