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Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism

文献类型：期刊论文

作者Shan, Shiguang1,2; Li, Yong3,4; Zeng, Jiabei4; Chen, Xilin3,4
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2019-05-01
卷号28期号:5页码:2439-2450
关键词Facial expression recognition occlusion CNN with attention mechanism gate unit
ISSN号1057-7149
DOI10.1109/TIP.2018.2886767
英文摘要Facial expression recognition in the wild is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect on analyzing constrained frontal faces, they fail to perform well on partially occluded faces that are common in the wild. In this paper, we propose a convolution neutral network (CNN) with attention mechanism (ACNN) that can perceive the occlusion regions of the face and focus on the most discriminative un-occluded regions. ACNN is an end-to-end learning framework. It combines the multiple representations from facial regions of interest (ROIs). Each representation is weighed via a proposed gate unit that computes an adaptive weight from the region itself according to the unobstructedness and importance. Considering different RoIs, we introduce two versions of ACNN: patch-based ACNN (pACNN) and global-local-based ACNN (gACNN). pACNN only pays attention to local facial patches. gACNN integrates local representations at patch-level with global representation at image-level. The proposed ACNNs are evaluated on both real and synthetic occlusions, including a self-collected facial expression dataset with real-world occlusions, the two largest in-the-wild facial expression datasets (RAF-DB and AffectNet) and their modifications with synthesized facial occlusions. Experimental results show that ACNNs improve the recognition accuracy on both the non-occluded faces and occluded faces. Visualization results demonstrate that, compared with the CNN without Gate Unit, ACNNs are capable of shifting the attention from the occluded patches to other related but unobstructed ones. ACNNs also outperform other state-of-the-art methods on several widely used in-the-lab facial expression datasets under the cross-dataset evaluation protocol.
资助项目National Key R&D Program of China[2017YFB1002802] ; Natural Science Foundation of China[61702481] ; Natural Science Foundation of China[61702486] ; External Cooperation Program of CAS[GJHZ1843]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000458850800004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/3422]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zeng, Jiabei
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Ctr Excellence Brain Sci & Intelligence Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Shan, Shiguang,Li, Yong,Zeng, Jiabei,et al. Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(5):2439-2450.
APA Shan, Shiguang,Li, Yong,Zeng, Jiabei,&Chen, Xilin.(2019).Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(5),2439-2450.
MLA Shan, Shiguang,et al."Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.5(2019):2439-2450.

入库方式： OAI收割

来源：计算技术研究所

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