  This paper demonstrates a novel approach to improve face-recognition
pose-invariance using semantic-segmentation features. The proposed
Seg-Distilled-ID network jointly learns identification and
semantic-segmentation tasks, where the segmentation task is then "distilled"
(MobileNet encoder). Performance is benchmarked against three state-of-the-art
encoders on a publicly available data-set emphasizing head-pose variations.
Experimental evaluations show the Seg-Distilled-ID network shows notable
robustness benefits, achieving 99.9% test-accuracy in comparison to 81.6% on
ResNet-101, 96.1% on VGG-19 and 96.3% on InceptionV3. This is achieved using
approximately one-tenth of the top encoder's inference parameters. These
results demonstrate distilling semantic-segmentation features can efficiently
address face-recognition pose-invariance.

Computer Vision and Pattern Recognition