Low Resolution Face Recogniton with Pose Variations Using Deep Belief Networks

摘要：

In practice face recognition sometimes encountered by low resolution (LR) face images with varying poses, which degrade the performance significantly. To address this problem, we propose an approach that applies deep belief network (DBN) to handle the nonlinearity caused by pose variations. The manifold assumption states that point-pairs from high resolution (HR) manifold share the topology with the corresponding LR manifold. Inspired by this assumption, we learn the relationship between HR manifold and LR manifold by sending both HR images and LR images to a deep architecture. High performance is achieved in the experiment on ORL and UMIST, in which great facial pose variations present.

关键词： face recognition pose variation low resolution deep belief network

作者: Miaozhen Lin Xin Fan

作者单位: School of Software Dalian University of Technology Dalian, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1543-1547

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）