计算机工程Issue(5)：228-233,6.DOI:10.3969/j.issn.1000-3428.2014.05.047
基于有监督降维的人脸识别方法
Face Recognition Method Based on Supervised Dimensionality Reduction
摘要
Abstract
Traditional dimensionality reduction methods only pay attention to the local similarity information of images. They neglect the diversity information of images and spatial structure of the pixels in the images. Therefore, a new supervised dimensionality reduction method is proposed, which constructs the local similarity graph and local diversity graph to characterize the local structure of images. Furthermore, a 2D Discretized Laplacian Smooth regularization by exploiting the spatial structure of the pixels in the images is introduced into the objective function. The method effectively maintains the local structure information between images and maintains the diversity information between images and spatial structure of the pixels in the images. It can effectively extract out the low dimensional feature from the face image. The method is verified on the Yale and ORL database, and experimental results show that the method has high recognition accuracy.关键词
降维/人脸识别/差异性/局部结构/空间结构/正则化Key words
dimensionality reduction/face recognition/diversity/local structure/spatial structure/regularization分类
信息技术与安全科学引用本文复制引用
姚明海,王娜,易玉根,栾敬钊..基于有监督降维的人脸识别方法[J].计算机工程,2014,(5):228-233,6.基金项目
吉林省科技发展计划青年科研基金资助项目(201201070)；辽宁省社会科学规划基金资助项目(L13BXW006)。 （201201070）
