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Switching Linear Inverse-Regression Model for Tracking Head Pose

By Vincent Drouard, Sileye Ba and Radu Horaud

Abstract

International audienceWe 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

Topics: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Publisher: 'Institute of Electrical and Electronics Engineers (IEEE)'
Year: 2017
DOI identifier: 10.1109/WACV.2017.142
OAI identifier: oai:HAL:hal-01430727v1
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