摘要
为了提高人脸的识别率及其识别速度,提出了一种基于Gabor特征与投影字典对学习的人脸识别算法。由于Gabor特征对表情、光照和角度等变化具有较强的鲁棒性,首先提取人脸图像多方向多尺度的Gabor局部特征,并将经主成分分析降维后的增广Gabor特征作为训练数据,代替原始的训练样本。然后,根据训练数据同时学习综合字典与分析字典,综合字典具有重构能力,分析字典可以快速求出系数矩阵。最后,根据各类别的重构误差进行分类,以达到人脸识别的目的。在扩展的YaleB、ORL和AR人脸数据库上的实验结果表明,提出的算法不仅具有较高的识别率,而且能够有效地提高识别速度。
To improve the recognition rate and speed of face recognition,we propose a face recognition algorithm based on Gabor feature and projective dictionary pair learning.We first extract the Gabor features of the image at multiple scales and orientations,which shows significant robustness to the variations in expression,illumination and angle.The augmented Gabor features with reduced dimension are achieved through principle component analysis,which constructs a new training data to replace original training samples.Then a synthesis dictionary with reconstruction capability and an analysis dictionary with the capability of quickly obtaining representation coefficients are learned jointly during the training phase.The face is eventually identified by the reconstructed errors.Experimental results on the extended YaleB,ORL and AR database show that the proposed algorithm can get a high recognition rate and improve the recognition speed efficiently.
出处
《计算机工程与科学》
CSCD
北大核心
2016年第3期542-548,共7页
Computer Engineering & Science
基金
国家自然科学基金(61402053)
湖南省教育厅优秀青年项目(12B003)
湖南省交通厅科技计划(201334)
2015年湖南省研究生科研创新项目(CX2015B369)
关键词
人脸识别
GABOR特征
综合字典
分析字典
face recognition
gabor feature
synthesis dictionary
analysis dictionary
