Name
Abstract | ||
|---|---|---|
In this paper, we target the Group-level emotion recognition sub-challenge of the fifth Emotion Recognition in the Wild (EmotiW 2017) Challenge, which is based on the Group Affect Database 2.0 containing images of groups of people in a wide variety of social events. We use Seetaface to detect and align the faces in the group images and extract two kinds of face-image visual features: VGGFace-lstm, DCNN-lstm. As group image features, we propose using Pyramid Histogram of Oriented Gradients (PHOG), CENTRIST, DCNN features, VGG features. To the testing group images on which the faces have been detected, the final emotion is estimated using group image features and face-level visual features. While to the testing group images on which the faces cannot be detected, the face-level visual features are fused for final recognition. The final achievements we have gained are 79.78% accuracy on the Group Affect Database 2.0 testing set, which is much higher than the corresponding baseline results 53.62%.
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Year | Venue | Field |
|---|---|---|
2017 | ICMI | Social group,Computer vision,Pattern recognition,Emotion recognition,Feature (computer vision),Computer science,Histogram of oriented gradients,Pyramid,Artificial intelligence,Deep learning,Group emotion |
DocType | ISBN | Citations |
Conference | 978-1-4503-5543-8 | 2 |
PageRank | References | Authors |
0.37 | 12 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinglan Wei | 1 | 27 | 2.44 |
| Yijia Zhao | 2 | 3 | 0.73 |
| Qihua Xu | 3 | 23 | 2.04 |
| Liandong Li | 4 | 77 | 5.02 |
| Jun He | 5 | 71 | 11.24 |
| Lejun Yu | 6 | 9 | 4.20 |
| Bo Sun | 7 | 104 | 21.35 |