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Conference papers

Learning Recurrent High-order Statistics for Skeleton-based Hand Gesture Recognition

Abstract : High-order statistics have been proven useful in the framework of Convolutional Neural Networks (CNN) for a variety of computer vision tasks. In this paper, we propose to exploit high-order statistics in the framework of Recurrent Neural Networks (RNN) for skeleton-based hand gesture recognition. Our method is based on the Statistical Recurrent Units (SRU), an un-gated architecture that has been introduced as an alternative model for Long-Short Term Memory (LSTM) and Gate Recurrent Unit (GRU). The SRU captures sequential information by generating recurrent statistics that depend on a context of previously seen data and by computing moving averages at different scales. The integration of high-order statistics in the SRU significantly improves the performance of the original one, resulting in a model that is competitive to state-of-the-art methods on the Dynamic Hand Gesture (DHG) dataset, and outperforms them on the First-Person Hand Action (FPHA) dataset.
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Contributor : Olivier Lezoray Connect in order to contact the contributor
Submitted on : Tuesday, January 12, 2021 - 6:02:00 PM
Last modification on : Saturday, June 25, 2022 - 9:56:33 AM
Long-term archiving on: : Tuesday, April 13, 2021 - 6:56:30 PM

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  • HAL Id : hal-03107675, version 1


Xuan Son Nguyen, Luc Brun, Olivier Lézoray, Sébastien Bougleux. Learning Recurrent High-order Statistics for Skeleton-based Hand Gesture Recognition. International Conference on Pattern Recognition (ICPR - IEEE), 2021, Milan (virtual), Italy. ⟨hal-03107675⟩



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