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Conference Papers Year : 2015

Head Pose Estimation via Probabilistic High-Dimensional Regression

Abstract

This paper addresses the problem of head pose estimation with three degrees of freedom (pitch, yaw, roll) from a single image. Pose estimation is formulated as a high-dimensional to low-dimensional mixture of linear regression problem. We propose a method that maps HOG-based descriptors, extracted from face bounding boxes, to corresponding head poses. To account for errors in the observed bounding-box position, we learn regression parameters such that a HOG descriptor is mapped onto the union of a head pose and an offset, such that the latter optimally shifts the bounding box towards the actual position of the face in the image. The performance of the proposed method is assessed on publicly available datasets. The experiments that we carried out show that a relatively small number of locally-linear regression functions is sufficient to deal with the non-linear mapping problem at hand. Comparisons with state-of-the-art methods show that our method outperforms several other techniques.
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Format : Figure, Image
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
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Dates and versions

hal-01163663 , version 1 (13-10-2015)

Identifiers

Cite

Vincent Drouard, Silèye Ba, Georgios Evangelidis, Antoine Deleforge, Radu Horaud. Head Pose Estimation via Probabilistic High-Dimensional Regression. IEEE International Conference on Image Processing, ICIP 2015, Sep 2015, Quebec City, QC, Canada. pp.4624-4628, ⟨10.1109/ICIP.2015.7351683⟩. ⟨hal-01163663⟩
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