Paper Info
Title
Group-Level Emotion Recognition using Deep Models with A Four-stream Hybrid Network.
Abstract
Group-level Emotion Recognition (GER) in the wild is a challenging task gaining lots of attention. Most recent works utilized two channels of information, a channel involving only faces and a channel containing the whole image, to solve this problem. However, modeling the relationship between faces and scene in a global image remains challenging. In this paper, we proposed a novel face-location aware global network, capturing the face location information in the form of an attention heatmap to better model such relationships. We also proposed a multi-scale face network to infer the group-level emotion from individual faces, which explicitly handles high variance in image and face size, as images in the wild are collected from different sources with different resolutions. In addition, a global blurred stream was developed to explicitly learn and extract the scene-only features. Finally, we proposed a four-stream hybrid network, consisting of the face-location aware global stream, the multi-scale face stream, a global blurred stream, and a global stream, to address the GER task, and showed the effectiveness of our method in GER sub-challenge, a part of the six Emotion Recognition in the Wild (EmotiW 2018) [10] Challenge. The proposed method achieved 65.59% and 78.39% accuracy on the testing and validation sets, respectively, and is ranked the third place on the leaderboard.
Year
DOI
Venue
2018
10.1145/3242969.3264987
ICMI
Keywords
Field
DocType
Emotion Recognition, Group-level emotion recognition, Affect Analysis, Attention Heatmap
Computer vision,Global network,Ranking,Pattern recognition,Computer science,Emotion recognition,Communication channel,Artificial intelligence
Conference
ISBN
Citations 
PageRank 
978-1-4503-5692-3
1
0.36
References 
Authors
37
6
Name
Order
Citations
PageRank
KHAN, AHMED-SHEHAB1313.47
Zhiyuan Li2308.40
Jie Cai3574.77
Zibo Meng424813.60
James O'Reilly5223.02
Yan Tong6111.85