Hybrid neural networks based approach for Holoscopic Micro-Gesture Recognition in images and videos

Garima Sharma, Shreyank Jyoti, Abhinav Dhall

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Subtitle of host publication15–19 May 2018 Xi’an, China
EditorsSidney D’Mello, Louis‐Philippe Morency, Michel Valstar, Lijun Yin
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781538623350
ISBN (Print)9781538623367
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Automatic Face and Gesture Recognition 2018 - Xi'an, China
Duration: 15 May 201819 May 2018
Conference number: 13th


ConferenceIEEE International Conference on Automatic Face and Gesture Recognition 2018
Abbreviated titleFG 2018
Internet address


  • Holoscopy
  • Micro gesture recognition

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