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  4. Extending explicit shape regression with mixed feature channels and pose priors
 
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Extending explicit shape regression with mixed feature channels and pose priors

Richter, Matthias
•
Gao, Hua  
•
Ekenel, Kemal Hazim
2014
Proceedings of IEEE Winter Conference on Applications of Computer Vision
2014 IEEE Winter Conference on Applications of Computer Vision (WACV)

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.

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wacv2014-RGE.pdf

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Publisher's Version

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openaccess

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2.21 MB

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Adobe PDF

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1a9064084bc08b572982336c3c6c6506

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