This paper presents a novel approach to address pose-invariant face frontalization aiming Multiview Facial Expression Recognition (MFER). Particularly, the proposed approach is a hybrid method, including both synthesizing and mapping techniques. The key idea is to use mapped reconstructive coefficients of each arbitrary viewpoints and the frontal bases where the mapping functions are provided by learning between frontal and non-frontal faces’ coefficients. We also exploit sparse coding for synthesizing the frontalized faces, even with large poses. For evaluation, two qualitative and quantitative assessments are used along with an application of multiview facial expression recognition as a case study. The results show that our approach is efficient in terms of frontalizing non-frontal faces. Moreover, its validation on two popular datasets, BU3DFE and Multi-PIE, with various assessments contexts reveals its efficiency and stability on head pose variation, especially on large poses.

中文翻译：

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一种用于多视图面部表情识别的联合映射和合成方法

本文提出了一种新的方法来解决针对多视图面部表情识别 (MFER) 的姿势不变面部正面化问题。特别是，所提出的方法是一种混合方法，包括合成和映射技术。关键思想是使用每个任意视点的映射重建系数和通过学习正面和非正面系数之间的映射函数来提供映射函数的正面基底。我们还利用稀疏编码来合成正面人脸，即使是大姿势。对于评估，使用了两种定性和定量评估以及多视图面部表情识别的应用作为案例研究。结果表明，我们的方法在正面化非正面方面是有效的。此外，它在两个流行的数据集上的验证，