Face Recognition Method Using Gabor Wavelet and Cross-covariance Dimensionality Reduction

LI Yaqian; ZHANG Shaowei; LI Haibin; ZHANG Wenming; ZHANG Qiang

doi:10.11999/JEIT161103

Volume 39 Issue 8

Aug. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(8): 2023-2027

LI Yaqian, ZHANG Shaowei, LI Haibin, ZHANG Wenming, ZHANG Qiang. Face Recognition Method Using Gabor Wavelet and Cross-covariance Dimensionality Reduction[J]. Journal of Electronics & Information Technology, 2017, 39(8): 2023-2027. doi: 10.11999/JEIT161103

Citation:

LI Yaqian, ZHANG Shaowei, LI Haibin, ZHANG Wenming, ZHANG Qiang. Face Recognition Method Using Gabor Wavelet and Cross-covariance Dimensionality Reduction[J]. Journal of Electronics & Information Technology, 2017, 39(8): 2023-2027. doi: 10.11999/JEIT161103

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Face Recognition Method Using Gabor Wavelet and Cross-covariance Dimensionality Reduction

doi: 10.11999/JEIT161103

1.
(Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China)
2.

Funds:

The Natural Science Foundation of Hebei Province (F2015203212)

Received Date: 2016-10-18
Rev Recd Date: 2017-03-01
Publish Date: 2017-08-19

Abstract

Abstract

The traditional face recognition is sensitive to light condition as well as facial expression, and has a shortcoming of high intra-group dispersion, a novel method is proposed to overcome these defects by combining Gabor wavelet and a weighted computation based on the cross-covariance. Firstly, Gabor features are extracted from the face image. Then, a weighted cross-covariance matrix is used for dimension reduction and feature extraction. Finally, the nearest neighbor classifier is performed for classification. Experimental results on the ORL face database and the AR face database show that the recognition performance of the proposed method is superior over the 2DPCA and its improved algorithm. It also reduces the dimensionality of feature and improves the recognition performance effectively.
- Face recognition,
- Gabor wavelet,
- 2-Dimensional Principal Component Analysis (2DPCA),
- Cross- covariance matrix

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References(14)

References

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