Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/636855
Title: Facial expression recognition via sparse representation using positive and reverse templates
Authors: Xingguo Jiang;Bin Feng;Liangnian Jin
subject: sparse representation classification method|positive and reverse templates|relevant expression databases|facial expression recognition
Year: 2016
Publisher: IEEE
Abstract: This study models facial expression recognition with a sparse representation classification (SRC) method. By analysing SRC's robustness to noise, this study further proposes an SRC method based on positive and reverse templates (PRTs-SRC), which uses PRTs to expand an over-complete dictionary constructed by training samples. The expanded dictionary can contain more information, and increase the robustness to noise. To validate the performance of the proposed algorithm, experiments were carried out on relevant expression databases. The authors compared and analysed the recognition performances for the proposed algorithm and other methods. The results show that even with high noise levels, the proposed algorithm performs above 80% recognition rate.
URI: http://localhost/handle/Hannan/172831
http://localhost/handle/Hannan/636855
ISSN: 1751-9659
1751-9667
volume: 10
issue: 8
Appears in Collections:2016

Files in This Item:
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Title: Facial expression recognition via sparse representation using positive and reverse templates
Authors: Xingguo Jiang;Bin Feng;Liangnian Jin
subject: sparse representation classification method|positive and reverse templates|relevant expression databases|facial expression recognition
Year: 2016
Publisher: IEEE
Abstract: This study models facial expression recognition with a sparse representation classification (SRC) method. By analysing SRC's robustness to noise, this study further proposes an SRC method based on positive and reverse templates (PRTs-SRC), which uses PRTs to expand an over-complete dictionary constructed by training samples. The expanded dictionary can contain more information, and increase the robustness to noise. To validate the performance of the proposed algorithm, experiments were carried out on relevant expression databases. The authors compared and analysed the recognition performances for the proposed algorithm and other methods. The results show that even with high noise levels, the proposed algorithm performs above 80% recognition rate.
URI: http://localhost/handle/Hannan/172831
http://localhost/handle/Hannan/636855
ISSN: 1751-9659
1751-9667
volume: 10
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7515374.pdf819.72 kBAdobe PDFThumbnail
Preview File
Title: Facial expression recognition via sparse representation using positive and reverse templates
Authors: Xingguo Jiang;Bin Feng;Liangnian Jin
subject: sparse representation classification method|positive and reverse templates|relevant expression databases|facial expression recognition
Year: 2016
Publisher: IEEE
Abstract: This study models facial expression recognition with a sparse representation classification (SRC) method. By analysing SRC's robustness to noise, this study further proposes an SRC method based on positive and reverse templates (PRTs-SRC), which uses PRTs to expand an over-complete dictionary constructed by training samples. The expanded dictionary can contain more information, and increase the robustness to noise. To validate the performance of the proposed algorithm, experiments were carried out on relevant expression databases. The authors compared and analysed the recognition performances for the proposed algorithm and other methods. The results show that even with high noise levels, the proposed algorithm performs above 80% recognition rate.
URI: http://localhost/handle/Hannan/172831
http://localhost/handle/Hannan/636855
ISSN: 1751-9659
1751-9667
volume: 10
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7515374.pdf819.72 kBAdobe PDFThumbnail
Preview File