Automatic two phase sparse representation method and face recognition experiments

Ke Yan; Yong Xu; Jian Zhang

doi:10.1007/978-3-319-12484-1_2

Automatic two phase sparse representation method and face recognition experiments

Ke Yan^*, Yong Xu, Jian Zhang

^*Corresponding author for this work

Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

The two phase sparse representation (TPSR) method has achieved promising face recognition performance. However, this method has the following flaw: its recognition accuracy varies with parameter Μ and at present there is no means to automatically set it. As a consequence, it becomes the bottleneck to apply the TPSR method to real-world problems. In this paper, we propose an improvement to TPSR (ITPSR), which can choose a proper value of parameter Μ for obtaining the optimal performance. Extensive experiments show that the proposed ITPSR is feasible and can obtain excellent performance.

Original language	English
Pages (from-to)	22-30
Number of pages	9
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8833
DOIs	https://doi.org/10.1007/978-3-319-12484-1_2
Publication status	Published - 2014
Externally published	Yes

Keywords

Pattern Recognition
Sparse Representation
Transform Methods

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10.1007/978-3-319-12484-1_2

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abstract = "The two phase sparse representation (TPSR) method has achieved promising face recognition performance. However, this method has the following flaw: its recognition accuracy varies with parameter Μ and at present there is no means to automatically set it. As a consequence, it becomes the bottleneck to apply the TPSR method to real-world problems. In this paper, we propose an improvement to TPSR (ITPSR), which can choose a proper value of parameter Μ for obtaining the optimal performance. Extensive experiments show that the proposed ITPSR is feasible and can obtain excellent performance.",

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T1 - Automatic two phase sparse representation method and face recognition experiments

AU - Yan, Ke

AU - Xu, Yong

AU - Zhang, Jian

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - The two phase sparse representation (TPSR) method has achieved promising face recognition performance. However, this method has the following flaw: its recognition accuracy varies with parameter Μ and at present there is no means to automatically set it. As a consequence, it becomes the bottleneck to apply the TPSR method to real-world problems. In this paper, we propose an improvement to TPSR (ITPSR), which can choose a proper value of parameter Μ for obtaining the optimal performance. Extensive experiments show that the proposed ITPSR is feasible and can obtain excellent performance.

AB - The two phase sparse representation (TPSR) method has achieved promising face recognition performance. However, this method has the following flaw: its recognition accuracy varies with parameter Μ and at present there is no means to automatically set it. As a consequence, it becomes the bottleneck to apply the TPSR method to real-world problems. In this paper, we propose an improvement to TPSR (ITPSR), which can choose a proper value of parameter Μ for obtaining the optimal performance. Extensive experiments show that the proposed ITPSR is feasible and can obtain excellent performance.

KW - Pattern Recognition

KW - Sparse Representation

KW - Transform Methods

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JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Automatic two phase sparse representation method and face recognition experiments

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Keywords

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