摘要
为消除可变光照对人脸识别的影响,提出一种基于正交Log-Gabor滤波二值模式(OLGBP)的人脸识别算法。该算法对样本在正交方向做Log-Gabor变换,然后将所得特征图像进行虚实分解和同尺度多方向二值融合构成OLGBP特征向量,再将这些特征向量构成协同表征字典D。最后,在字典D下对测试样本采用协同表征求稀疏系数,并通过误差重构来分类。在AR、Extend Yale B和CAS-PEAL-R1人脸数据库上的实验结果表明,OLGBP算法对光照变化的单样本人脸识别具有较好的效果,从而验证了算法的有效性。
To eliminate the effect of varying illumination on face recognition,a novel method of face recognition based on orthogonal log-Gabor binary pattern(OLGBP)is proposed in this paper.First,the algorithm performs log-Gabor transform on the samples in the orthogonal direction.Then the log-Gabor feature image is decomposed into real and imaginary parts,and the OLGBP feature vectors are constructed by fusing them into a binary pattern in the same scale at different directions.These feature vectors then form a collaboratively representative dictionary D.Finally,sparse coefficients are obtained by collaboratively representing these feature vectors with the test samples based on the dictionary D,and the test samples are classified by reconstruction of errors.The results for experiments performed on AR,Extend Yale B,and CAS-PEAL-R1 face databases show that the OLGBP algorithm has good effect on a single sample with illumination variation,and the effectiveness of the algorithm is verified.
作者
杨恢先
付宇
曾金芳
徐唱
YANG Huixian;FU Yu;ZENG Jinfang;XU Chang(School of Physics and Optoelectronic,Xiangtan University,Xiangtan 411105,China)
出处
《智能系统学报》
CSCD
北大核心
2019年第2期330-337,共8页
CAAI Transactions on Intelligent Systems
基金
湘潭大学博士启动基金项目(KZ07089)
湘潭大学校级科研项目(16XZX02)
