Multi-View Face Recognition Via Well-Advised Pose Normalization Network

Numerous face frontalization methods based on 3D Morphable Model (3DMM) and Generative Adversarial Networks (GAN) have made great progress in multi-view face recognition. However, facial feature analysis and identity discrimination often suffer from failure frontalization results because of monotonous single-domain training and unpredictable input profile faces. To overcome the drawback, we present a novel approach named Well-advised Pose Normalization Network (WAPNN), which leverages multiple domains and extracts features considering their frontalization qualities wisely, to achieve a high accuracy on multi-view face recognition. Through multi-domain datasets, we design an end-to-end facial pose normalization network with adaptive weights on different objectives to exploit potentialities of various profile-front relationships. Meanwhile, the proposed method encourages intra-class compactness and inter-class separability between facial features by introducing quality-aware feature fusion. Experimental analyses show that our method effectively recovers frontal faces with good-quality textures and high identity-preserving, and significantly reduces the impact of various poses on face recognition under both constrained and wild environments.