Towards a practical face recognition system: Robust registration and illumination by sparse representation

Andrew Wagner, John Wright, Arvind Ganesh, Zihan Zhou, Yi Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

171 Citations (Scopus)

Abstract

Most contemporary face recognition algorithms work well under laboratory conditions but degrade when tested in less-controlled environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, alignment, pose, and occlusion. In this paper, we propose a simple and practical face recognition system that achieves a high degree of robustness and stability to all these variations. We demonstrate how to use tools from sparse representation to align a test face image with a set of frontal training images in the presence of significant registration error and occlusion. We thoroughly characterize the region of attraction for our alignment algorithm on public face datasets such as Multi-PIE. We further study how to obtain a sufficient set of training illuminations for linearly interpolating practical lighting conditions. We have implemented a complete face recognition system, including a projectorbased training acquisition system, in order to evaluate how our algorithms work under practical testing conditions. We show that our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.

Original languageEnglish (US)
Title of host publication2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
PublisherIEEE Computer Society
Pages597-604
Number of pages8
ISBN (Print)9781424439935
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Miami, FL, United States
Duration: Jun 20 2009Jun 25 2009

Publication series

Name2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
Volume2009 IEEE Computer Society Conference on Computer Vision and ...

Other

Other2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CountryUnited States
CityMiami, FL
Period6/20/096/25/09

Fingerprint

Face recognition
Lighting
Testing

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

Cite this

Wagner, A., Wright, J., Ganesh, A., Zhou, Z., & Ma, Y. (2009). Towards a practical face recognition system: Robust registration and illumination by sparse representation. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 (pp. 597-604). [5206654] (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009; Vol. 2009 IEEE Computer Society Conference on Computer Vision and ...). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2009.5206654
Wagner, Andrew ; Wright, John ; Ganesh, Arvind ; Zhou, Zihan ; Ma, Yi. / Towards a practical face recognition system : Robust registration and illumination by sparse representation. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society, 2009. pp. 597-604 (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009).
@inproceedings{bef5c82a5da846d7b61bff45c9d655e2,
title = "Towards a practical face recognition system: Robust registration and illumination by sparse representation",
abstract = "Most contemporary face recognition algorithms work well under laboratory conditions but degrade when tested in less-controlled environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, alignment, pose, and occlusion. In this paper, we propose a simple and practical face recognition system that achieves a high degree of robustness and stability to all these variations. We demonstrate how to use tools from sparse representation to align a test face image with a set of frontal training images in the presence of significant registration error and occlusion. We thoroughly characterize the region of attraction for our alignment algorithm on public face datasets such as Multi-PIE. We further study how to obtain a sufficient set of training illuminations for linearly interpolating practical lighting conditions. We have implemented a complete face recognition system, including a projectorbased training acquisition system, in order to evaluate how our algorithms work under practical testing conditions. We show that our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.",
author = "Andrew Wagner and John Wright and Arvind Ganesh and Zihan Zhou and Yi Ma",
year = "2009",
month = "1",
day = "1",
doi = "10.1109/CVPRW.2009.5206654",
language = "English (US)",
isbn = "9781424439935",
series = "2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009",
publisher = "IEEE Computer Society",
pages = "597--604",
booktitle = "2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009",
address = "United States",

}

Wagner, A, Wright, J, Ganesh, A, Zhou, Z & Ma, Y 2009, Towards a practical face recognition system: Robust registration and illumination by sparse representation. in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009., 5206654, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, vol. 2009 IEEE Computer Society Conference on Computer Vision and ..., IEEE Computer Society, pp. 597-604, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL, United States, 6/20/09. https://doi.org/10.1109/CVPRW.2009.5206654

Towards a practical face recognition system : Robust registration and illumination by sparse representation. / Wagner, Andrew; Wright, John; Ganesh, Arvind; Zhou, Zihan; Ma, Yi.

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society, 2009. p. 597-604 5206654 (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009; Vol. 2009 IEEE Computer Society Conference on Computer Vision and ...).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Towards a practical face recognition system

T2 - Robust registration and illumination by sparse representation

AU - Wagner, Andrew

AU - Wright, John

AU - Ganesh, Arvind

AU - Zhou, Zihan

AU - Ma, Yi

PY - 2009/1/1

Y1 - 2009/1/1

N2 - Most contemporary face recognition algorithms work well under laboratory conditions but degrade when tested in less-controlled environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, alignment, pose, and occlusion. In this paper, we propose a simple and practical face recognition system that achieves a high degree of robustness and stability to all these variations. We demonstrate how to use tools from sparse representation to align a test face image with a set of frontal training images in the presence of significant registration error and occlusion. We thoroughly characterize the region of attraction for our alignment algorithm on public face datasets such as Multi-PIE. We further study how to obtain a sufficient set of training illuminations for linearly interpolating practical lighting conditions. We have implemented a complete face recognition system, including a projectorbased training acquisition system, in order to evaluate how our algorithms work under practical testing conditions. We show that our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.

AB - Most contemporary face recognition algorithms work well under laboratory conditions but degrade when tested in less-controlled environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, alignment, pose, and occlusion. In this paper, we propose a simple and practical face recognition system that achieves a high degree of robustness and stability to all these variations. We demonstrate how to use tools from sparse representation to align a test face image with a set of frontal training images in the presence of significant registration error and occlusion. We thoroughly characterize the region of attraction for our alignment algorithm on public face datasets such as Multi-PIE. We further study how to obtain a sufficient set of training illuminations for linearly interpolating practical lighting conditions. We have implemented a complete face recognition system, including a projectorbased training acquisition system, in order to evaluate how our algorithms work under practical testing conditions. We show that our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.

UR - http://www.scopus.com/inward/record.url?scp=70450162109&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450162109&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2009.5206654

DO - 10.1109/CVPRW.2009.5206654

M3 - Conference contribution

AN - SCOPUS:70450162109

SN - 9781424439935

T3 - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

SP - 597

EP - 604

BT - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

PB - IEEE Computer Society

ER -

Wagner A, Wright J, Ganesh A, Zhou Z, Ma Y. Towards a practical face recognition system: Robust registration and illumination by sparse representation. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society. 2009. p. 597-604. 5206654. (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009). https://doi.org/10.1109/CVPRW.2009.5206654