Gabor feature based classification using Enhance Two-direction Variation of 2DPCA discriminant analysis for face verification

Hsi Kuan Chen, Yi Chun Lee, Chin-Hsing Chen

研究成果: Paper

8 引文 (Scopus)

摘要

This paper derives and implements a new technique called horizontal and vertical Enhance Gabor discriminant analysis (HVGD) for image representation and recognition. In this approach, we firstly use Gabor wavelets to extract local features at different frequencies and orientations from facial images. The horizontal and vertical principal component analysis (HVPCA) is then applied directly on the Gabor transformed matrices to reduce sensitivity to imprecise eye detection and face cropping. To improve upon the traditional discriminant analysis methods for face verification, the enhanced Fisher linear discriminant model (EFM) method is finally applied to further remove redundant information and form a discriminant representation more suitable for face recognition. The results show that the HVGD method performs better than the PCA, the FLD, and the EFM. The top recognition accuracy of our proposed method can reach 97.7% on the Yale database.

原文English
頁面541-548
頁數8
DOIs
出版狀態Published - 2013 五月 27
事件2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013 - Kaohsiung, Taiwan
持續時間: 2013 二月 252013 二月 26

Other

Other2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013
國家Taiwan
城市Kaohsiung
期間13-02-2513-02-26

指紋

Discriminant analysis
Face recognition
Principal component analysis

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

引用此文

Chen, H. K., Lee, Y. C., & Chen, C-H. (2013). Gabor feature based classification using Enhance Two-direction Variation of 2DPCA discriminant analysis for face verification. 541-548. 論文發表於 2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013, Kaohsiung, Taiwan. https://doi.org/10.1109/ISNE.2013.6512419
Chen, Hsi Kuan ; Lee, Yi Chun ; Chen, Chin-Hsing. / Gabor feature based classification using Enhance Two-direction Variation of 2DPCA discriminant analysis for face verification. 論文發表於 2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013, Kaohsiung, Taiwan.8 p.
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abstract = "This paper derives and implements a new technique called horizontal and vertical Enhance Gabor discriminant analysis (HVGD) for image representation and recognition. In this approach, we firstly use Gabor wavelets to extract local features at different frequencies and orientations from facial images. The horizontal and vertical principal component analysis (HVPCA) is then applied directly on the Gabor transformed matrices to reduce sensitivity to imprecise eye detection and face cropping. To improve upon the traditional discriminant analysis methods for face verification, the enhanced Fisher linear discriminant model (EFM) method is finally applied to further remove redundant information and form a discriminant representation more suitable for face recognition. The results show that the HVGD method performs better than the PCA, the FLD, and the EFM. The top recognition accuracy of our proposed method can reach 97.7{\%} on the Yale database.",
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Chen, HK, Lee, YC & Chen, C-H 2013, 'Gabor feature based classification using Enhance Two-direction Variation of 2DPCA discriminant analysis for face verification' 論文發表於 2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013, Kaohsiung, Taiwan, 13-02-25 - 13-02-26, 頁 541-548. https://doi.org/10.1109/ISNE.2013.6512419

Gabor feature based classification using Enhance Two-direction Variation of 2DPCA discriminant analysis for face verification. / Chen, Hsi Kuan; Lee, Yi Chun; Chen, Chin-Hsing.

2013. 541-548 論文發表於 2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013, Kaohsiung, Taiwan.

研究成果: Paper

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AB - This paper derives and implements a new technique called horizontal and vertical Enhance Gabor discriminant analysis (HVGD) for image representation and recognition. In this approach, we firstly use Gabor wavelets to extract local features at different frequencies and orientations from facial images. The horizontal and vertical principal component analysis (HVPCA) is then applied directly on the Gabor transformed matrices to reduce sensitivity to imprecise eye detection and face cropping. To improve upon the traditional discriminant analysis methods for face verification, the enhanced Fisher linear discriminant model (EFM) method is finally applied to further remove redundant information and form a discriminant representation more suitable for face recognition. The results show that the HVGD method performs better than the PCA, the FLD, and the EFM. The top recognition accuracy of our proposed method can reach 97.7% on the Yale database.

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Chen HK, Lee YC, Chen C-H. Gabor feature based classification using Enhance Two-direction Variation of 2DPCA discriminant analysis for face verification. 2013. 論文發表於 2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013, Kaohsiung, Taiwan. https://doi.org/10.1109/ISNE.2013.6512419