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Group-Level Emotion Recognition Using Hybrid Deep Models Based on Faces, Scenes, Skeletons and Visual Attentions

Year: 2018

Type: conference-paper

Abstract: This paper presents a hybrid deep learning network submitted to the 6th Emotion Recognition in the Wild (EmotiW 2018) Grand Challenge [9], in the category of group-level emotion recognition. Advanced deep learning models trained individually on faces, scenes, skeletons and salient regions using visual attention mechanisms are fused to classify the emotion of a group of people in an image as positive, neutral or negative. Experimental results show that the proposed hybrid network achieves 78.98% and 68.08% classification accuracy on the validation and testing sets, respectively. These results outperform the baseline of 64% and 61%, and achieved the first place in the challenge.

Source: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

DOI: 10.1145/3242969.3264990

Authors: Xin Guo, Bin Zhu, Luisa F. Polanía, Charles Boncelet, Kenneth E. Barner

Institution: University of Delaware

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: HCC: Small: Head Activated Technology for Off-the-Shelf Mobile Devices