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一種基于Gabor小波及互協(xié)方差降維運算的人臉識別方法

李雅倩,  張少偉,  李海濱,  張文明,  張強

李雅倩, 張少偉, 李海濱, 張文明, 張強. 一種基于Gabor小波及互協(xié)方差降維運算的人臉識別方法[J]. 電子與信息學報, 2017, 39(8): 2023-2027. doi: 10.11999/JEIT161103
引用本文: 李雅倩, 張少偉, 李海濱, 張文明, 張強. 一種基于Gabor小波及互協(xié)方差降維運算的人臉識別方法[J]. 電子與信息學報, 2017, 39(8): 2023-2027. doi: 10.11999/JEIT161103
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

一種基于Gabor小波及互協(xié)方差降維運算的人臉識別方法

doi: 10.11999/JEIT161103
  • 1.

    (燕山大學工業(yè)計算機控制工程河北省重點實驗室 秦皇島 066004) ②(東北大學信息科學與工程學院 沈陽 110819)

基金項目: 

河北省自然科學基金(F2015203212)

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

  • 1.

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

Funds: 

The Natural Science Foundation of Hebei Province (F2015203212)

  • 摘要: 針對傳統(tǒng)的人臉識別方法對人臉圖像的曝光量、表情比較敏感，并且具有較大類內(nèi)離散度的缺點，該文提出一種基于Gabor小波以及加權(quán)互協(xié)方差運算的人臉識別算法。該算法首先對人臉圖像提取Gabor特征，然后使用加權(quán)的互協(xié)方差矩陣對經(jīng)過處理的特征圖像進行降維及特征提取；最后使用最近鄰分類器進行分類。在ORL數(shù)據(jù)庫和AR數(shù)據(jù)庫上的實驗表明，該方法的降維和識別性能優(yōu)于傳統(tǒng)2DPCA及其改進算法，能兼顧維度簡約性和準確性，有效地提高了識別性能。
    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.
    • HTML全文
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出版歷程
  • 收稿日期:  2016-10-18
  • 修回日期:  2017-03-01
  • 刊出日期:  2017-08-19

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