Extending explicit shape regression with mixed feature channels and pose priors

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.


Published in:
Proceedings of IEEE Winter Conference on Applications of Computer Vision, 1013-1019
Presented at:
2014 IEEE Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, CO, USA, March 24-26, 2014
Year:
2014
Publisher:
IEEE
Keywords:
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 Record created 2014-07-30, last modified 2018-12-03

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