Frontal Face Generation from Multiple Pose-Variant Faces with CGAN in Real-World Surveillance Scene
Zhu-Liang Chen, Qian-Hua He, Wen-Feng Pang, Yan-Xiong Li
Abstract
It is well known that frontal face is much easier to be recognized than pose-variant face for both human and machine perception. However, it is not easy to acquire a frontal face in real-world video surveillance. This paper proposes a method to synthetize a frontal face for recognition in video surveillance scene, which is based on Conditional Generative Adversarial Networks (cGAN) with input of multiple pose-variant faces from a video. Experimental results show that the proposed approach can generate suitable frontal faces and improve face recognition by around 20% on a dataset of 43276 face images from 19 persons, collected from the real-world video surveillance scene. The effectiveness of multiple frames against single frame as input is demonstrated. Moreover, we investigate the generator with different depth for synthetizing frontal faces, in which an up-down sampling trick is designed for synthetizing higher quality frontal face images and boosts the performance of the generator.
BibTeX
@inproceedings{icassp2018_frontalfacegener,
title = {Frontal Face Generation from Multiple Pose-Variant Faces with CGAN in Real-World Surveillance Scene},
author = {Zhu-Liang Chen and Qian-Hua He and Wen-Feng Pang and Yan-Xiong Li},
booktitle = {ICASSP 2018},
year = {2018}
}