We introduce our method and system for face recognition using multiple pose-aware deep learning models. In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features. 3D rendering is used to generate multiple face poses from the input image. Sensitivity of the recognition system to pose variations is reduced since we use an ensemble of pose-specific CNN features. The paper presents extensive experimental results on the effect of landmark detection, CNN layer selection and pose model selection on the performance of the recognition pipeline. Our novel representation achieves better results than the state-of-the-art on IARPA's CS2 and NIST's IJB-A in both verification and identification (i.e. search) tasks.
@article{arxiv.1603.07388,
title = {Face Recognition Using Deep Multi-Pose Representations},
author = {Wael AbdAlmageed and Yue Wua and Stephen Rawlsa and Shai Harel and Tal Hassner and Iacopo Masi and Jongmoo Choi and Jatuporn Toy Leksut and Jungyeon Kim and Prem Natarajan and Ram Nevatia and Gerard Medioni},
journal= {arXiv preprint arXiv:1603.07388},
year = {2016}
}