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A Hybrid CRF/HMM for One-Shot Gesture Learning

Selma Belgacem 1 Clément Chatelain 2 Thierry Paquet 2
1 MAIA - Autonomous intelligent machine
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : This chapter deals with the characterization and the recognition of human gestures in videos. We propose a global characterization of gestures that we call the Gesture Signature. The gesture signature describes the location, velocity, and orientation of the global motion of a gesture deduced from optical flows. The proposed hybrid CRF/HMM model combines the modelling ability of hidden Markov models and the discriminative ability of conditional random fields. We applied this hybrid system to the recognition of gesture in videos in the context of one-shot learning, where only one sample gesture per class is given to train the system. In this rather extreme context, the proposed framework achieves very interesting performance which suggests its application to other biometric recognition tasks.
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Contributor : Thierry Paquet <>
Submitted on : Thursday, March 21, 2019 - 3:41:08 PM
Last modification on : Tuesday, November 24, 2020 - 9:38:05 AM



Selma Belgacem, Clément Chatelain, Thierry Paquet. A Hybrid CRF/HMM for One-Shot Gesture Learning. Ajita Rattani; Fabio Roli; Eric Granger. Adaptive Biometric Systems: Recent Advances and Challenges, Springer, pp.51-72, 2015, 978-3-319-24865-3. ⟨10.1007/978-3-319-24865-3_4⟩. ⟨hal-02075745⟩



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