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Facial images in surveillance or mobile scenarios often have large view-pointvariations in terms of pitch and yaw angles. These jointly occurred anglevariations make face recognition challenging. Current public face databasesmainly consider the case of yaw variations. In this paper, a new large-scaleMulti-yaw Multi-pitch high-quality database is proposed for Facial PoseAnalysis (M2FPA), including face frontalization, face rotation, facial poseestimation and pose-invariant face recognition. It contains 397,544 images of229 subjects with yaw, pitch, attribute, illumination and accessory. M2FPA isthe most comprehensive multi-view face database for facial pose analysis.Further, we provide an effective benchmark for face frontalization andpose-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 andbenchmark can significantly push forward the advance of facial pose analysis inreal-world applications. Moreover, a simple yet effective parsing guideddiscriminator is introduced to capture the local consistency during GANoptimization. Extensive quantitative and qualitative results on M2FPA andMulti-PIE demonstrate the superiority of our face frontalization method.Baseline results for both face synthesis and face recognition fromstate-of-theart methods demonstrate the challenge offered by this new database.

M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis