Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2013

Abstract

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.

Keywords

coupling alignments with recognition; still-to-video face recognition

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, December 1-8.

First Page

3296

Last Page

3303

ISBN

9781479928392

Identifier

10.1109/ICCV.2013.409

Publisher

Institute of Electrical and Electronics Engineers Inc.

City or Country

New York

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