Still-to-video face recognition in unconstrained environments

Haoyu Wang; Changsong Liu; Xiaoqing Ding

doi:10.1117/12.2082990

27 February 2015 Still-to-video face recognition in unconstrained environments

Haoyu Wang, Changsong Liu, Xiaoqing Ding

Proceedings Volume 9405, Image Processing: Machine Vision Applications VIII; 94050O (2015) https://doi.org/10.1117/12.2082990
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

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.

Citation Download Citation

Haoyu Wang, Changsong Liu, and Xiaoqing Ding "Still-to-video face recognition in unconstrained environments", Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050O (27 February 2015); https://doi.org/10.1117/12.2082990

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available