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  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  and AFLW , 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.