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Low-resolution face recognition (LR FR) aims to recognize faces from small size or poor quality images with varying pose, illumination, expression, etc. It has received much attention with increasing demands for long distance surveillance applications, and extensive efforts have been made on LR FR research in recent years. However, many issues in LR FR are still unsolved, such as super-resolution (SR) for face recognition, resolution-robust features, unified feature spaces, and face detection at a distance, although many methods have been developed for that. This paper provides a comprehensive survey on these methods and discusses many related issues. First, it gives an overview on LR FR, including concept description, system architecture, and method categorization. Second, many representative methods are broadly reviewed and discussed. They are classified into two different categories, super-resolution for LR FR and resolution-robust feature representation for LR FR. Their strategies and advantages/disadvantages are elaborated. Some relevant issues such as databases and evaluations for LR FR are also presented. By generalizing their performances and limitations, promising trends and crucial issues for future research are finally discussed. [ABSTRACT FROM AUTHOR]

In this paper, a novel video matting method based on opacity propagation is proposed. It adopts a two-step framework by first segmenting each frame into bilayer and generating trimaps, and then applying matting algorithm to extract the final alpha mattes. In the bilayer segmentation step, an opacity propagation algorithm is used to predict the foreground object in the following frame. It propagates the opacity values of the former frame to the current, and generates an Opacity Map (OM), which is in most cases much more accurate than the previously used Probability Map (PM). OM is used to extract the foreground object by combining it with the graph cut algorithm. And then an accurate trimap is generated by a trimap refinement method based on a Local Gaussian Mixture Model (LGMM). At last, the opacity propagation is again applied to the unknown regions of the trimap to generate the accurate alpha mattes in a temporally coherent way. The experiment results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]