Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M2FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition. It contains 397,544 images of 229 subjects with yaw, pitch, attribute, illumination and accessory. M2FPA is the most comprehensive multi-view face database for facial pose analysis. Further, we provide an effective benchmark for face frontalization and pose-invariant face recognition on M2FPA with several state-of-the-art methods, including DR-GAN, TP-GAN and CAPG-GAN. We believe that the new database and benchmark can significantly push forward the advance of facial pose analysis in real-world applications. Moreover, a simple yet effective parsing guided discriminator is introduced to capture the local consistency during GAN optimization. Extensive quantitative and qualitative results on M2FPA and Multi-PIE demonstrate the superiority of our face frontalization method. Baseline results for both face synthesis and face recognition from state-of-theart methods demonstrate the challenge offered by this new database.

本文提出了一种新的大规模多偏航多俯仰高质量数据库（M2FPA），用于面部姿势分析，其中包括人脸正面化，人脸旋转，面部姿势估计和姿态不变人脸识别。M2FPA是最全面的多视角人脸数据库，此外，我们提供了M2FPA上的人脸正面化和姿态不变人脸识别的有效基准，包括DR-GAN，TP-GAN和CAPG-GAN等state-of-the-art方法，并介绍了一种简单而有效的解析引导鉴别器来捕获GAN优化期间的局部一致性，M2FPA和Multi-PIE的广泛定量和定性结果展示了我们人脸正面化方法的优越性。此外，state-of-the-art方法在人脸生成和人脸识别方面的基础结果展示了此新数据库的挑战。

M2FPA：一种多偏航角多俯仰角高质量的面部姿态分析数据库和基准