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

作者：Dornaika F^*; Chahla C; Khattar F; Abdallah F; Snoussi H

来源：Engineering Applications of Artificial Intelligence, 2016, 50: 168-176.

DOI：10.1016/j.engappai.2016.01.035

• Model-less face pose estimation
• Manifold learning
• Locality preserving projections
• Sparse coding
• Out-of-plan rotation

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.

• 出版日期2016-4