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Recursive Head Reconstruction from Multi-View Video Sequences

Abstract : Face reconstruction from images has been a core topic for the last decades, and is now involved in many applications such as identity verification or human–computer interaction. The 3D Morphable Model introduced by Blanz and Vetter has been widely used to this end, because its specific 3D modeling offers robustness to pose variation and adaptability to the specificities of each face. To overcome the limitations of methods using a single image, and since video has become more and more affordable, we propose a new method which exploits video sequences to consolidate the 3D head shape estimation using successive frames. Based on particle filtering, our algorithm updates the model estimation at each instant and it is robust to noisy observations. A comparison with the Levenberg– Marquardt global optimization approach on various sets of data shows visual improvements both on pose and shape estimation. Biometric performances confirm this trend with a mean reduction of 10% in terms of False Rejection Rate.
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Submitted on : Friday, September 13, 2019 - 4:15:45 PM
Last modification on : Monday, June 27, 2022 - 12:30:32 PM


  • HAL Id : hal-02286842, version 1


Catherine Herold, Vincent Despiegel, Stéphane Gentric, Séverine Dubuisson, Isabelle Bloch. Recursive Head Reconstruction from Multi-View Video Sequences. Computer Vision and Image Understanding, Elsevier, 2014, 122, pp.182-201. ⟨hal-02286842⟩



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