Self-supervised random mask attention GAN in tackling pose-invariant face recognition

Abstract

Pose Invariant Face Recognition (PIFR) has significantly advanced with Generative Adversarial Networks (GANs), which rotate face images acquired at any angle to a frontal view for enhanced recognition. However, such frontalization methods typically need ground-truth frontal-view images, often collected under strict laboratory conditions, making it challenging and costly to acquire the necessary training data. Additionally, traditional self-supervised PIFR methods rely on external rendering models for training, further complicating the overall training process. To tackle these two issues, we propose a new framework called Mask Rotate. Our framework introduces a novel training approach that requires no paired ground truth data for the face image frontalization task. Moreover, it eliminates the need for an external rendering model during training. Specifically, our framework simplifies the face image frontalization task by transforming it into a face image completion task. During the inference or testing stage, it employs a reliable pre-trained rendering model to obtain a frontal-view face image, which may have several regions with missing texture due to pose variations and occlusion. Our framework then uses a novel self-supervised Random Mask Attention Generative Adversarial Network (RMAGAN) to fill in these missing regions by considering them as randomly masked regions. Furthermore, our proposed Mask Rotate framework uses a reliable post-processing model designed to improve the visual quality of the face images after frontalization. In comprehensive experiments, the Mask Rotate framework eliminates the requirement for complex computations during training and achieves strong results, both qualitative and quantitative, compared to the state-of-the-art.