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 25 → 2013 二月 26 |
Other
| Other | 2013 IEEE International Symposium on Next-Generation Electronics, ISNE 2013 |
|---|---|
| 國家 | Taiwan |
| 城市 | Kaohsiung |
| 期間 | 13-02-25 → 13-02-26 |
指紋
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
引用此文
}
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
TY - CONF
T1 - Gabor feature based classification using Enhance Two-direction Variation of 2DPCA discriminant analysis for face verification
AU - Chen, Hsi Kuan
AU - Lee, Yi Chun
AU - Chen, Chin-Hsing
PY - 2013/5/27
Y1 - 2013/5/27
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=84877948818&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877948818&partnerID=8YFLogxK
U2 - 10.1109/ISNE.2013.6512419
DO - 10.1109/ISNE.2013.6512419
M3 - Paper
AN - SCOPUS:84877948818
SP - 541
EP - 548
ER -