eMotion-GAN: A Motion-based GAN for Photorealistic and Facial Expression Preserving Frontal View Synthesis
This addresses a specific issue in facial expression recognition systems for applications requiring accurate analysis under non-frontal head poses, representing a domain-specific incremental advancement.
The paper tackles the problem of facial expression recognition performance degradation due to head pose variations by proposing eMotion-GAN, a method for frontal view synthesis that preserves facial expressions, resulting in FER improvements of up to +5% for small pose variations and up to +20% for larger ones.
Many existing facial expression recognition (FER) systems encounter substantial performance degradation when faced with variations in head pose. Numerous frontalization methods have been proposed to enhance these systems' performance under such conditions. However, they often introduce undesirable deformations, rendering them less suitable for precise facial expression analysis. In this paper, we present eMotion-GAN, a novel deep learning approach designed for frontal view synthesis while preserving facial expressions within the motion domain. Considering the motion induced by head variation as noise and the motion induced by facial expression as the relevant information, our model is trained to filter out the noisy motion in order to retain only the motion related to facial expression. The filtered motion is then mapped onto a neutral frontal face to generate the corresponding expressive frontal face. We conducted extensive evaluations using several widely recognized dynamic FER datasets, which encompass sequences exhibiting various degrees of head pose variations in both intensity and orientation. Our results demonstrate the effectiveness of our approach in significantly reducing the FER performance gap between frontal and non-frontal faces. Specifically, we achieved a FER improvement of up to +5\% for small pose variations and up to +20\% improvement for larger pose variations. Code available at \url{https://github.com/o-ikne/eMotion-GAN.git}.