One shot similarity metric learning for action recognition
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Finding correspondence from multiple images via sparse and low-rank decomposition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Flow counting using realboosted multi-sized window detectors
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., 'in the wild'), the 'YouTube Faces' database, along with benchmark, pair-matching tests^1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.