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融合Gabor 特征与投影字典对学习的#br# 人脸识别算法

  

  • 出版日期:2016-04-28 发布日期:2016-05-20

Face Recognition Methods Fusing Gabor Feature and#br# Projective Dictionary Pair Learning

  • Online:2016-04-28 Published:2016-05-20

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摘要： 为了获得更好的人脸特征，有效地提高算法的识别率，提出了一种联合Gabor 特征
与投影字典对学习的人脸识别算法G-DPL。算法使用Gabor 小波提取人脸图像的局部特征，对特
征向量使用PCA 与LDA 的方法进行降维。将投影字典对学习算法与降维后的Gabor 特征融合，
然后进行分类识别。提出的G-DPL 算法在ORL 库上整体识别率达到99.00%，特征维数为39 维。
在AR 库上识别率达到96.14%，特征维数为99 维。提出的G-DPL 算法在占用较少空间的同时能
够获得更高的识别率，对实际应用具有一定的参考价值。

关键词: 人脸识别, Gabor, 投影字典对

Abstract: In order to obtain better face features and enhance the recognition rate of algorithm, a face
recognition algorithm based on Gabor feature and projective dictionary pair learing named G-DPL is
proposed in this paper. The local feature of face image are extracted by Gabor wavelet and PCA and LDA
scheme is used to reduce the feature dimension. Projective dictionary pair learning algorithm and
dimensionality reduced Gabor feature are fused to identify the classification. The recognition rate of G-DPL
algorithm can reach 99.00% under ORL database. Feature dimensionality is 39. G-DPL can reach 96.14%
on AR database. Feature dimensionality is 99. The proposed G-DPL algorithm can obtain higher recognition
rate while taking up less space, which has certain reference value for practical application.

Key words: face recognition, Gabor, projective dictionary pair