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Communication Dans Un Congrès Année : 2013

3D Face Pose Tracking from Monocular Camera via Sparse Representation of Synthesized Faces

Résumé

This paper presents a new method to track head pose efficiently from monocular camera via sparse representation of synthesized faces. In our framework, the appearance model is trained using a database of synthesized face generated from the first video frame. The pose estimation is based on the similarity distance between the observations of landmarks and their reconstructions. The reconstruction is the texture extracted around the landmark, represented as a sparse linear combination of positive training samples after solving l1-norm problem. The approach finds the position of new landmarks and face pose by minimizing an energy function as the sum of these distances while simultaneously constraining the shape by a 3D face. Our framework gives encouraging pose estimation results on the Boston University Face Tracking (BUFT) dataset.

Domaines

Informatique
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Dates et versions

hal-00957737 , version 1 (10-03-2014)

Identifiants

  • HAL Id : hal-00957737 , version 1

Citer

Ngoc-Trung Tran, Jacques Feldmar, Maurice Charbit, Dijana Petrovska-Delacrétaz, Gérard Chollet. 3D Face Pose Tracking from Monocular Camera via Sparse Representation of Synthesized Faces. VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications, Volume 2, Feb 2013, Barcelona, Spain. pp.328-333. ⟨hal-00957737⟩
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