Abstract | ||
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The Still-to-Video (S2V) face recognition systems typically need to match faces in low-quality videos captured under unconstrained conditions against high quality still face images, which is very challenging because of noise, image blur, low face resolutions, varying head pose, complex lighting, and alignment difficulty. To address the problem, one solution is to select the frames of `best quality' from videos (hereinafter called quality alignment in this paper). Meanwhile, the faces in the selected frames should also be geometrically aligned to the still faces offline well-aligned in the gallery. In this paper, we discover that the interactions among the three tasks-quality alignment, geometric alignment and face recognition-can benefit from each other, thus should be performed jointly. With this in mind, we propose a Coupling Alignments with Recognition (CAR) method to tightly couple these tasks via low-rank regularized sparse representation in a unified framework. Our method makes the three tasks promote mutually by a joint optimization in an Augmented Lagrange Multiplier routine. Extensive experiments on two challenging S2V datasets demonstrate that our method outperforms the state-of-the-art methods impressively. |
Year | DOI | Venue |
---|---|---|
2013 | 10.1109/ICCV.2013.409 | ICCV |
Keywords | Field | DocType |
coupling alignments,still-to-video face recognition,tasks-quality alignment,alignment difficulty,quality alignment,geometric alignment,face recognition system,low face resolution,best quality,s2v datasets,high quality,state-of-the-art method,image restoration,face recognition | Facial recognition system,Computer vision,3D single-object recognition,Coupling,Object-class detection,Three-dimensional face recognition,Pattern recognition,Computer science,Sparse approximation,Artificial intelligence,Face detection,Image restoration | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1550-5499 |
Citations | PageRank | References |
10 | 0.53 | 15 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhiwu Huang | 1 | 252 | 15.26 |
Xiaowei Zhao | 2 | 26 | 1.10 |
Shiguang Shan | 3 | 6322 | 283.75 |
Ruiping Wang | 4 | 894 | 41.60 |
Xilin Chen | 5 | 6291 | 306.27 |