A Hybrid CRF/HMM for One-Shot Gesture Learning

Selma Belgacem (1) , Clément Chatelain (2) , Thierry Paquet (2)

1 MAIA - Autonomous intelligent machine
2 DocApp - LITIS - Equipe Apprentissage

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.

Mots clés: Hide Markov Model; Feature Vector; Optical Flow; Gesture Recognition; Hide State

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

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⟩