We present a probabilistic generative model for tracking by prediction the dynamics of affective spacial expressions in videos. The model relies on Bayesian filter sampling of facial landmarks conditioned on motor action parameter dynamics; namely, trajectories shaped by an autoregressive Gaussian Process Latent Variable state-space. The analysis-by-synthesis approach at the heart of the model allows for both inference and generation of affective expressions. Robustness of the method to occlusions and degradation of video quality has been assessed on a publicly available dataset.
Predictive Sampling of Facial Expression Dynamics Driven by a Latent Action Space / G. Boccignone, M. Bodini, V. Cuculo, G. Grossi - In: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / [a cura di] G.S. DiBaja, L. Gallo, K. Yetongnon, A. Dipanda, M. CastrillonSantana, R. Chbeir. - Prima edizione. - [s.l] : IEEE, 2019 May. - ISBN 9781538693858. - pp. 143-150 (( Intervento presentato al 14. convegno International Conference on Signal Image Technology & Internet Based Systems (SITIS) tenutosi a Las Palmas de Gran Canaria nel 2018.
Predictive Sampling of Facial Expression Dynamics Driven by a Latent Action Space
G. Boccignone;M. Bodini;V. Cuculo;G. Grossi
2019
Abstract
We present a probabilistic generative model for tracking by prediction the dynamics of affective spacial expressions in videos. The model relies on Bayesian filter sampling of facial landmarks conditioned on motor action parameter dynamics; namely, trajectories shaped by an autoregressive Gaussian Process Latent Variable state-space. The analysis-by-synthesis approach at the heart of the model allows for both inference and generation of affective expressions. Robustness of the method to occlusions and degradation of video quality has been assessed on a publicly available dataset.File | Dimensione | Formato | |
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