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Discriminant sparse label-sensitive embedding: Application to image-based face pose estimation

Abstract : In this letter, the authors propose a new embedding scheme for image-based continuous face pose estimation. The main contributions are as follows. First, it is shown that the concept of label-sensitive Locality Preserving Projections, proposed for age estimation, can be used for model-less face pose estimation. Second, the authors propose a linear embedding by exploiting the connections between facial features and pose labels via a sparse coding scheme. The resulting technique is called Sparse Label sensitive Locality Preserving Projections (Sp-LsLPP). Third, for enhancing the discrimination between poses, the projections obtained by Sp-LsLPP are fed to a Discriminant Embedding that exploits the continuous labels. The resulting framework has less parameters compared to related works. It has been applied to the problem of model-less face yaw angle estimation (person independent 3D face pose estimation). It was tested on three databases: FacePix, Taiwan, and Columbia. It was conveniently compared with other linear and non-linear techniques. The experimental results confirm that the proposed framework can outperform, in general, the existing ones.
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Contributor : Jean-Baptiste VU VAN Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:44:05 AM
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F. Dornaika, Charbel Chahla, F. Khattar, F. Abdallah, Hichem Snoussi. Discriminant sparse label-sensitive embedding: Application to image-based face pose estimation. Engineering Applications of Artificial Intelligence, Elsevier, 2016, 50, pp.168-176. ⟨10.1016/j.engappai.2016.01.035⟩. ⟨hal-03320665⟩



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