Extending explicit shape regression with mixed feature channels and pose priors

Matthias Richter; Hua Gao; Hazim Kemal Ekenel

doi:10.1109/WACV.2014.6835993

Extending explicit shape regression with mixed feature channels and pose priors

Matthias Richter, Hua Gao, Hazim Kemal Ekenel

Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Facial feature detection offers a wide range of applications, e.g. in facial image processing, human computer interaction, consumer electronics, and the entertainment industry. These applications impose two antagonistic key requirements: high processing speed and high detection accuracy. We address both by expanding upon the recently proposed explicit shape regression [1] to (a) allow usage and mixture of different feature channels, and (b) include head pose information to improve detection performance in non-cooperative environments. Using the publicly available 'wild' datasets LFW [10] and AFLW [11], we show that using these extensions outperforms the baseline (up to 10% gain in accuracy at 8% IOD) as well as other state-of-the-art methods.

Original language	English (US)
Title of host publication	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Publisher	IEEE Computer Society
Pages	1013-1019
Number of pages	7
ISBN (Print)	9781479949854
DOIs	https://doi.org/10.1109/WACV.2014.6835993
State	Published - 2014
Event	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States Duration: Mar 24 2014 → Mar 26 2014

Publication series

Name	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Conference

Conference	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Country/Territory	United States
City	Steamboat Springs, CO
Period	3/24/14 → 3/26/14

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/WACV.2014.6835993

Cite this

Richter, M., Gao, H., & Ekenel, H. K. (2014). Extending explicit shape regression with mixed feature channels and pose priors. In 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 1013-1019). Article 6835993 (2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014). IEEE Computer Society. https://doi.org/10.1109/WACV.2014.6835993

Extending explicit shape regression with mixed feature channels and pose priors. / Richter, Matthias; Gao, Hua; Ekenel, Hazim Kemal.
2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. IEEE Computer Society, 2014. p. 1013-1019 6835993 (2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Richter, M, Gao, H & Ekenel, HK 2014, Extending explicit shape regression with mixed feature channels and pose priors. in 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014., 6835993, 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, IEEE Computer Society, pp. 1013-1019, 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, Steamboat Springs, CO, United States, 3/24/14. https://doi.org/10.1109/WACV.2014.6835993

@inproceedings{ed24eecd6c794ff4a3f7c0bcdc6bb42f,

title = "Extending explicit shape regression with mixed feature channels and pose priors",

abstract = "Facial feature detection offers a wide range of applications, e.g. in facial image processing, human computer interaction, consumer electronics, and the entertainment industry. These applications impose two antagonistic key requirements: high processing speed and high detection accuracy. We address both by expanding upon the recently proposed explicit shape regression [1] to (a) allow usage and mixture of different feature channels, and (b) include head pose information to improve detection performance in non-cooperative environments. Using the publicly available 'wild' datasets LFW [10] and AFLW [11], we show that using these extensions outperforms the baseline (up to 10% gain in accuracy at 8% IOD) as well as other state-of-the-art methods.",

author = "Matthias Richter and Hua Gao and Ekenel, {Hazim Kemal}",

year = "2014",

doi = "10.1109/WACV.2014.6835993",

language = "English (US)",

isbn = "9781479949854",

series = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014",

publisher = "IEEE Computer Society",

pages = "1013--1019",

booktitle = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014",

note = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 ; Conference date: 24-03-2014 Through 26-03-2014",

}

TY - GEN

T1 - Extending explicit shape regression with mixed feature channels and pose priors

AU - Richter, Matthias

AU - Gao, Hua

AU - Ekenel, Hazim Kemal

PY - 2014

Y1 - 2014

N2 - Facial feature detection offers a wide range of applications, e.g. in facial image processing, human computer interaction, consumer electronics, and the entertainment industry. These applications impose two antagonistic key requirements: high processing speed and high detection accuracy. We address both by expanding upon the recently proposed explicit shape regression [1] to (a) allow usage and mixture of different feature channels, and (b) include head pose information to improve detection performance in non-cooperative environments. Using the publicly available 'wild' datasets LFW [10] and AFLW [11], we show that using these extensions outperforms the baseline (up to 10% gain in accuracy at 8% IOD) as well as other state-of-the-art methods.

AB - Facial feature detection offers a wide range of applications, e.g. in facial image processing, human computer interaction, consumer electronics, and the entertainment industry. These applications impose two antagonistic key requirements: high processing speed and high detection accuracy. We address both by expanding upon the recently proposed explicit shape regression [1] to (a) allow usage and mixture of different feature channels, and (b) include head pose information to improve detection performance in non-cooperative environments. Using the publicly available 'wild' datasets LFW [10] and AFLW [11], we show that using these extensions outperforms the baseline (up to 10% gain in accuracy at 8% IOD) as well as other state-of-the-art methods.

UR - http://www.scopus.com/inward/record.url?scp=84904668241&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904668241&partnerID=8YFLogxK

U2 - 10.1109/WACV.2014.6835993

DO - 10.1109/WACV.2014.6835993

M3 - Conference contribution

AN - SCOPUS:84904668241

SN - 9781479949854

T3 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

SP - 1013

EP - 1019

BT - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

PB - IEEE Computer Society

T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Y2 - 24 March 2014 through 26 March 2014

ER -

Extending explicit shape regression with mixed feature channels and pose priors

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this