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Author Yong Xu ♦ Xiaozhao Fang ♦ Xuelong Li ♦ Jiang Yang ♦ You, J. ♦ Hong Liu ♦ Shaohua Teng
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science ♦ Natural sciences & mathematics ♦ Physics ♦ Electricity & electronics ♦ Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Training ♦ Face ♦ Uncertainty ♦ Face recognition ♦ Databases ♦ Lighting ♦ Accuracy ♦ uncertainty ♦ Computer vision ♦ face recognition ♦ machine learning ♦ pattern recognition ♦ uncertainty ♦ Computer vision ♦ face recognition ♦ machine learning ♦ pattern recognition
Abstract The image of a face varies with the illumination, pose, and facial expression, thus we say that a single face image is of high uncertainty for representing the face. In this sense, a face image is just an observation and it should not be considered as the absolutely accurate representation of the face. As more face images from the same person provide more observations of the face, more face images may be useful for reducing the uncertainty of the representation of the face and improving the accuracy of face recognition. However, in a real world face recognition system, a subject usually has only a limited number of available face images and thus there is high uncertainty. In this paper, we attempt to improve the face recognition accuracy by reducing the uncertainty. First, we reduce the uncertainty of the face representation by synthesizing the virtual training samples. Then, we select useful training samples that are similar to the test sample from the set of all the original and synthesized virtual training samples. Moreover, we state a theorem that determines the upper bound of the number of useful training samples. Finally, we devise a representation approach based on the selected useful training samples to perform face recognition. Experimental results on five widely used face databases demonstrate that our proposed approach can not only obtain a high face recognition accuracy, but also has a lower computational complexity than the other state-of-the-art approaches.
Description Author affiliation :: Bio-Comput. Res. Center, Harbin Inst. of Technol., Shenzhen, China
Author affiliation :: Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China
Author affiliation :: State Key Lab. of Transient Opt. & Photonics, Xi'an Inst. of Opt. & Precision Mech., Xi'an, China
Author affiliation :: Guangdong Univ. of Technol., Guangzhou, China
Author affiliation :: Eng. Lab. on Intell. Perception for Internet of Things, Peking Univ., Shenzhen, China
Author affiliation :: Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. of Sci. & Technol., Nanjing, China
ISSN 21682267
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 44
Issue Number 10
Size (in Bytes) 16.80 MB
Page Count 12
Starting Page 1950
Ending Page 1961

Source: IEEE Xplore Digital Library