摘要
非约束环境下采集的人脸图像复杂多变,因稀疏保留投影(Sparse Preserving Projection,SPP)算法没有考虑到样本的局部结构使其降维效果不理想,针对该问题,本文提出了加权判别稀疏保留投影(Weighted Discriminant Sparse Preserving Projection,WDSPP)算法。首先,引入样本类别标签和类内紧凑项,用以增强待测样本和同类样本之间的重构关系;其次,非控环境下样本质量参差不齐,考虑以样本距离权值约束稀疏重构系数,降低同类奇异样本的影响,进一步提高重构关系的准确度;最后,低维投影阶段增加全局约束因子,利用样本全局分布中隐含的鉴别信息使低维子空间分布更紧凑、更易于鉴别。在AR库、Extended Yale B库、LFW库和PubFig库上的大量实验结果表明,本文所提算法在复杂人脸环境下具有较好的识别结果。
The face images acquired in the unconstrained environment are complex and changeable,and sparse preserving projection(SPP)algorithm is not ideal for dimensionality reduction due to lack of sample local structure,in view of this problem,a weighted discriminant sparse preserving projection for unconstrained face recognition is proposed.Firstly,in order to enhance the reconstruction relationship between query sample and training samples of the same type,and class label information of samples and the in-class compact item are added.Secondly,due to the uneven quality of the sample under non-control environment,the sample distance weight is used to constrain the sparse reconstruction coefficient,which reduces the influence of similar singular samples and further improves the accuracy of the reconstruction relationship.Finally,the global constraint factor is added in the low-dimensional projection process,and the low-dimensional subspace distribution is made more compact and more discriminative by using the implicit identification information in the global distribution of the sample.Some experiments results show the method achieves better recognition on the AR,the Extended Yale B,the LFW and the PubFig databases.
作者
王志强
童莹
曹雪虹
任丽
Wang Zhiqiang;Tong Ying;Cao Xuehong;Ren Li(College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing,Jiangsu 210003,China;School of Information and Communication Engineering,Nanjing Institute of Technology,Nanjing,Jiangsu 211167,China)
出处
《信号处理》
CSCD
北大核心
2019年第10期1762-1772,共11页
Journal of Signal Processing
基金
国家自然科学基金(61703201)
江苏省自然科学基金(BK20170765)
南京工程学院创新基金(CKJB201602)
南京工程学院高层次引进人才科研启动基金资助(YKJ201862)
关键词
非约束人脸识别
稀疏保留投影
加权稀疏表示
降维
unconstrained face recognition
sparse preserving projection
weighted sparse representation
dimensionality reduction
