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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	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.
AbstractList	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.
Author	Zhang, D. Jing-Yu Yang Jian Yang Yong Xu
Author_xml	– sequence: 1 surname: Yong Xu fullname: Yong Xu email: laterfa112@yahoo.com.cn organization: Bio-Comput. Res. Center, Harbin Inst. of Technol., Shenzhen, China – sequence: 2 givenname: D. surname: Zhang fullname: Zhang, D. email: csdzhang@comp.polyu.edu.hk organization: Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China – sequence: 3 surname: Jian Yang fullname: Jian Yang email: csjyang@mail.njust.edu.cn organization: Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China – sequence: 4 surname: Jing-Yu Yang fullname: Jing-Yu Yang email: yangjy@mai.njust.edu.cn organization: Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
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Keywords	Learning Biometrics Nearest neighbour Computer vision transform methods Linear combination Face recognition Image processing Test method Sparse representation Pattern recognition Automatic recognition
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Snippet	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...
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SubjectTerms	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
Title	A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition
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