A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition

In this paper, we propose a two-phase test sample representation method for face recognition. The first phase of the proposed method seeks to represent the test sample as a linear combination of all the training samples and exploits the representation ability of each training sample to determine M &...

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Vydáno v:IEEE transactions on circuits and systems for video technology Ročník 21; číslo 9; s. 1255 - 1262
Hlavní autoři: Yong Xu, Zhang, D., Jian Yang, Jing-Yu Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.09.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Applied sciences
Artificial intelligence
Circuits
Classification
Computer science; control theory; systems
Computer vision
Electronic mail
Exact sciences and technology
Face recognition
Image processing
Information, signal and communications theory
Materials
Nearest neighbor searches
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Principal component analysis
Representations
Signal processing
sparse representation
Studies
Telecommunications and information theory
Training
transform methods
Transforms
Learning
Biometrics
Nearest neighbour
Computer vision
transform methods
Linear combination
Face recognition
Image processing
Test method
Sparse representation
Pattern recognition
Automatic recognition
ISSN:1051-8215, 1558-2205
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Popis
Shrnutí:In this paper, we propose a two-phase test sample representation method for face recognition. The first phase of the proposed method seeks to represent the test sample as a linear combination of all the training samples and exploits the representation ability of each training sample to determine M "nearest neighbors" for the test sample. The second phase represents the test sample as a linear combination of the determined M nearest neighbors and uses the representation result to perform classification. We propose this method with the following assumption: the test sample and its some neighbors are probably from the same class. Thus, we use the first phase to detect the training samples that are far from the test sample and assume that these samples have no effects on the ultimate classification decision. This is helpful to accurately classify the test sample. We will also show the probability explanation of the proposed method. A number of face recognition experiments show that our method performs very well.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2011.2138790