15 November 2011 Face recognition based tensor structure
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Proceedings Volume 8335, 2012 International Workshop on Image Processing and Optical Engineering; 83350E (2011) https://doi.org/10.1117/12.917596
Event: 2012 International Workshop on Image Processing and Optical Engineering, 2012, Harbin, China
Abstract
Face recognition has broad applications, and it is a difficult problem since face image can change with photographic conditions, such as different illumination conditions, pose changes and camera angles. How to obtain some invariable features for a face image is the key issue for a face recognition algorithm. In this paper, a novel tensor structure of face image is proposed to represent image features with eight directions for a pixel value. The invariable feature of the face image is then obtained from gradient decomposition to make up the tensor structure. Then the singular value decomposition (SVD) and principal component analysis (PCA) of this tensor structure are used for face recognition. The experimental results from this study show that many difficultly recognized samples can correctly be recognized, and the recognition rate is increased by 9%-11% in comparison with same type of algorithms.
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De-qiang Yang, De-qiang Yang, Zhi-xia Ye, Zhi-xia Ye, Yang Zhao, Yang Zhao, Li-mei Liu, Li-mei Liu, "Face recognition based tensor structure", Proc. SPIE 8335, 2012 International Workshop on Image Processing and Optical Engineering, 83350E (15 November 2011); doi: 10.1117/12.917596; https://doi.org/10.1117/12.917596
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