Face Recognition Using Deep Multi-Pose Representations
Abstract
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.
Cite
Text
Abd-Almageed et al. "Face Recognition Using Deep Multi-Pose Representations." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477555Markdown
[Abd-Almageed et al. "Face Recognition Using Deep Multi-Pose Representations." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/abdalmageed2016wacv-face/) doi:10.1109/WACV.2016.7477555BibTeX
@inproceedings{abdalmageed2016wacv-face,
title = {{Face Recognition Using Deep Multi-Pose Representations}},
author = {Abd-Almageed, Wael and Wu, Yue and Rawls, Stephen and Harel, Shai and Hassner, Tal and Masi, Iacopo and Choi, Jongmoo and Leksut, Jatuporn Toy and Kim, Jungyeon and Natarajan, Prem and Nevatia, Ram and Medioni, Gérard G.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-9},
doi = {10.1109/WACV.2016.7477555},
url = {https://mlanthology.org/wacv/2016/abdalmageed2016wacv-face/}
}