Richter, MatthiasGao, HuaEkenel, Kemal Hazim2014-07-302014-07-302014-07-30201410.1109/WACV.2014.6835993https://infoscience.epfl.ch/handle/20.500.14299/105371Facial 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.shape regressionpose estimationfeature extractionfacial feature detectiontext::conference output::conference proceedings::conference paper