人工智能
计算机科学
三维人脸识别
模式识别(心理学)
分类器(UML)
表达式(计算机科学)
面部表情
计算机视觉
不变(物理)
面部识别系统
水准点(测量)
面子(社会学概念)
人脸检测
姿势
数学
社会学
数学物理
社会科学
大地测量学
程序设计语言
地理
作者
Feifei Zhang,Tianzhu Zhang,Qirong Mao,Changsheng Xu
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:29: 4445-4460
被引量:84
标识
DOI:10.1109/tip.2020.2972114
摘要
Driven by recent advances in human-centered computing, Facial Expression Recognition (FER) has attracted significant attention in many applications. However, most conventional approaches either perform face frontalization on a non-frontal facial image or learn separate classifier for each pose. Different from existing methods, this paper proposes an end-to-end deep learning model that allows to simultaneous facial image synthesis and pose-invariant facial expression recognition by exploiting shape geometry of the face image. The proposed model is based on generative adversarial network (GAN) and enjoys several merits. First, given an input face and a target pose and expression designated by a set of facial landmarks, an identity-preserving face can be generated through guiding by the target pose and expression. Second, the identity representation is explicitly disentangled from both expression and pose variations through the shape geometry delivered by facial landmarks. Third, our model can automatically generate face images with different expressions and poses in a continuous way to enlarge and enrich the training set for the FER task. Our approach is demonstrated to perform well when compared with state-of-the-art algorithms on both controlled and in-the-wild benchmark datasets including Multi-PIE, BU-3DFE, and SFEW. The code is included in the supplementary material.
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