Mr. Haoyu Wang Profile

Haoyu Wang

at Tsinghua Univ

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Proceedings Article | 27 February 2015 Paper

Still-to-video face recognition in unconstrained environments

Haoyu Wang, Changsong Liu, Xiaoqing Ding

Proceedings Volume 9405, 94050O (2015) https://doi.org/10.1117/12.2082990

KEYWORDS: Video, Video surveillance, Facial recognition systems, Video compression, Image resolution, Detection and tracking algorithms, Light sources and illumination, Digital cameras, Associative arrays, Intelligence systems

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Face images from video sequences captured in unconstrained environments usually contain several kinds of variations, e.g. pose, facial expression, illumination, image resolution and occlusion. Motion blur and compression artifacts also deteriorate recognition performance. Besides, in various practical systems such as law enforcement, video surveillance and e-passport identification, only a single still image per person is enrolled as the gallery set. Many existing methods may fail to work due to variations in face appearances and the limit of available gallery samples. In this paper, we propose a novel approach for still-to-video face recognition in unconstrained environments. By assuming that faces from still images and video frames share the same identity space, a regularized least squares regression method is utilized to tackle the multi-modality problem. Regularization terms based on heuristic assumptions are enrolled to avoid overfitting. In order to deal with the single image per person problem, we exploit face variations learned from training sets to synthesize virtual samples for gallery samples. We adopt a learning algorithm combining both affine/convex hull-based approach and regularizations to match image sets. Experimental results on a real-world dataset consisting of unconstrained video sequences demonstrate that our method outperforms the state-of-the-art methods impressively.

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