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The study of face frontalization is essential for improving face recognition accuracy in extreme pose scenarios. Mainstream methods like TP-GAN, CAPG-GAN, etc., have made meaningful contributions. However, they still suffer from two problems: the lack of extracted feature diversity and the blurred details in generated images. This paper proposes a pre-trained feature fusion and multi-domain identification generative adversarial network (PM-GAN) for face frontalization: the features of the model pre-trained on large-scale datasets are fused with the original features of the encoder to enhance the diversity and robustness of features. In order to fuse features more effectively, we design a novel feature fusion module (FFM). In addition, a group of global and local discriminators is introduced to reinforce the local details and realism of generated frontal faces. Experimental results show that our proposed method outperforms state-of-the-art methods on M<sup>2</sup>FPA and CAS-PEAL datasets.

Pre-Trained Feature Fusion and Multidomain Identification Generative Adversarial Network for Face Frontalization