关键词: 低分辨人脸识别, 图像超分辨, 耦合映射, 字典学习, 稀疏表示, 神经网络

Abstract: This paper comprehensively surveys the research progress of Low-Resolution（LR） face recognition methods, and discusses the unsolved issues. Firstly, the fundamental concepts, the unsolved issues, the system’s structure, and the existing LR face recognition methods are presented, respectively. Secondly, in terms of the characteristics of mismatching between the High-Resolution（HR） and LR face images, two major categories of LR face recognition methods based on Super-Resolution（SR） image and common feature subspace are introduced in detail. According to different implementation processes, each category method is further divided into three different types, and the main ideas and core issues of each kind of method are analyzed and discussed. Then nine kinds of standard face databases are briefly introduced, and the experimental results of each representative method on the introduced face databases are analyzed from the aspects of recognition performance, average running time, and experimental comparisons performed on several face databases. Finally, the difficulties to be solved in the future development of LR face recognition technology are prospected.

Key words: low-resolution face recognition, image super-resolution, coupled mapping, dictionary learning, sparse representation, neural network