Switching Linear Inverse-Regression Model for Tracking Head Pose

Vincent Drouard 1 Sileye Ba 1 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We propose to estimate the head-pose angles (pitch, yaw, and roll) by simultaneously predicting the pose parameters from observed high-dimensional feature vectors, and tracking these parameters over time. This is achieved by embedding a Gaussian mixture of linear inverse-regression model into a dynamic Bayesian model. The use of a switching Kalman filter (SKF) enables a principled way of carrying out this embedding. The SKF governs the temporal predic-tive distribution of the pose parameters (modeled as continuous latent variables) conditioned by the discrete variables associated with the mixture of linear inverse-regression formulation. We formally derive the equations of the proposed switching linear regression model, we propose an approximation that is both identifiable and computation-ally tractable, we design an EM procedure to estimate the SKF parameters in closed-form, and we carry out experiments and comparisons with other methods using recently released datasets.
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Submitted on : Tuesday, January 10, 2017 - 10:58:02 AM
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Vincent Drouard, Sileye Ba, Radu Horaud. Switching Linear Inverse-Regression Model for Tracking Head Pose. IEEE Winter Conference on Applications of Computer Vision, Mar 2017, Santa Rosa, CA, United States. pp.1232-1240, ⟨10.1109/WACV.2017.142⟩. ⟨hal-01430727⟩

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