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Face recognition performance degrades considerably when the input images are of Low Resolution (LR), as is often the case for images taken by surveillance cameras or from a large distance. In this paper, we propose a novel approach for matching low-resolution probe images with higher resolution gallery images, which are often available during enrollment, using Multidimensional Scaling (MDS). The ideal scenario is when both the probe and gallery images are of high enough resolution to discriminate across different subjects. The proposed method simultaneously embeds the low-resolution probe images and the high-resolution gallery images in a common space such that the distance between them in the transformed space approximates the distance had both the images been of high resolution. The two mappings are learned simultaneously from high-resolution training images using an iterative majorization algorithm. Extensive evaluation of the proposed approach on the Multi-PIE data set with probe image resolution as low as 8 6 pixels illustrates the usefulness of the method. We show that the proposed approach improves the matching performance significantly as compared to performing matching in the low-resolution domain or using super-resolution techniques to obtain a higher resolution test image prior to recognition. Experiments on low-resolution surveillance images from the Surveillance Cameras Face Database further highlight the effectiveness of the approach.

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Driven by key law enforcement and commercial applications, research on face recognition from video sources has intensified in recent years. The ensuing results have demonstrated that videos possess unique properties that allow both humans and automated systems to perform recognition accurately in difficult viewing conditions. However, significant research challenges remain as most video-based applications do not allow for controlled recordings. In this survey, we categorize the research in this area and present a broad and deep review of recently proposed methods for overcoming the difficulties encountered in unconstrained settings. We also draw connections between the ways in which humans and current algorithms recognize faces. An overview of the most popular and difficult publicly available face video databases is provided to complement these discussions. Finally, we cover key research challenges and opportunities that lie ahead for the field as a whole.