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Journal Articles IEEE Transactions on Image Processing Year : 2017

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions

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Abstract

Head-pose estimation has many applications, such as social-event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging because it must cope with changing illumination conditions, face orientation and appearance variabilities, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment problems. We propose a mixture of linear regression method that learns how to map high-dimensional feature vectors (extracted from bounding-boxes of faces) onto both head-pose parameters and bounding-box shifts, such that at runtime they are simultaneously predicted. We describe in detail the mapping method that combines the merits of manifold learning and of mixture of linear regression. We validate our method with three publicly available datasets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.
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Dates and versions

hal-01413406 , version 1 (01-02-2017)

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Vincent Drouard, Radu Horaud, Antoine Deleforge, Sileye Ba, Georgios Evangelidis. Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions. IEEE Transactions on Image Processing, 2017, 26 (3), pp.1428 - 1440. ⟨10.1109/TIP.2017.2654165⟩. ⟨hal-01413406⟩
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