Abstract
This paper presents an approach for hand based micro-gesture recognition in images and videos as part of the Holoscopic Micro-Gesture Recognition (HoMGR) challenge. The database consists of Holoscopic 3D Micro-Gesture images and videos. The proposed framework is an ensemble of convolutional neural network and deep neural network. The framework performs feature fusion technique on both handcrafted (local phase quantization) and deep features extracted from the neural network, to leverage on complimentary information. The powerful discriminative nature of the fused features has proved beneficial on the given HoMGR challenge data. The experiments show that the proposed approach is effective and outperforms the baseline on the Test set by an absolute margin of 26.67% for images and 2.47% for videos, respectively.
Original language | English |
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Title of host publication | Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 |
Subtitle of host publication | 15–19 May 2018 Xi’an, China |
Editors | Sidney D’Mello, Louis‐Philippe Morency, Michel Valstar, Lijun Yin |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 808-814 |
Number of pages | 7 |
ISBN (Electronic) | 9781538623350 |
ISBN (Print) | 9781538623367 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | IEEE International Conference on Automatic Face and Gesture Recognition 2018 - Xi'an, China Duration: 15 May 2018 → 19 May 2018 Conference number: 13th https://fg2018.cse.sc.edu/ |
Conference
Conference | IEEE International Conference on Automatic Face and Gesture Recognition 2018 |
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Abbreviated title | FG 2018 |
Country/Territory | China |
City | Xi'an |
Period | 15/05/18 → 19/05/18 |
Internet address |
Keywords
- Holoscopy
- Micro gesture recognition