摘要
针对人脸图片的遮挡、伪装、光照及表情变化等问题,根据Gabor特征对遮挡、伪装、光照及表情变化有着更强的鲁棒性的特点,提出了联合Gabor误差字典和低秩表示的人脸识别算法(GDLRR)。首先对训练样本和测试样本分别进行Gabor特征提取,并将这些特征组成待测试的特征字典;然后将一个单位阵进行Gabor特征提取并训练成一个更紧凑的Gabor误差字典;最后联合Gabor误差字典和训练特征字典对测试特征字典进行低秩表示后进行分类识别。各类实验表明,提出的改进算法对人脸识别的各类问题都有着更强的鲁棒性和更高的识别准确率。
Focused on the issues that face images have the problems occlusion, disguise,illumination and facial expres- sion changes in face recognition, an improved face recognition method was proposed. According to the characteristics of the Gabor feature for occlusion, disguise, illumination and facial expression changes has stronger robustness, jointing Gabor error dictionary and low rank representation （GDLRR） for face recognition was proposed. Firstly, the Gabor fea- ture of training samples and testing samples are extracted for making up features dictionaries that is to be tested, re- spectively. And then,a unit matrix is utilized to extract Gabor feature for training a more compact Gabor error dictiona- ry. Finally, lowest-rank representation of feature dictionary of testing samples is sought for classification by jointing Ga- bor error dictionary and training feature dictionary. Experiments show that the proposed algorithm has better robust- ness and recognition results against the different problems in the face recognition.
作者
首照宇
杨晓帆
SHOU Zhao-yu YANG Xiao-fan(Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin 541004, China)
出处
《计算机科学》
CSCD
北大核心
2017年第3期296-299,共4页
Computer Science
基金
广西自然科学基金(2013GXNSFDA019030
2013GXNSFAA019331
2014GXN SFDA118035)
桂林市科技攻关项目(20130105-6
20140103-5)
桂林电子科技大学研究生科研创新项目(YJCXS201531)资助
关键词
遮挡
低秩表示
GABOR特征
误差字典
降维
Occlusion, Low-rank representation, Gabor feature, Error dictionary, Reduce dimension
