Daily monitoring of intra-personal facial changes associated with health and emotional conditions has great potential to be useful for medical, healthcare, and emotion recognition fields. However, the approach for capturing intra-personal facial changes is relatively unexplored due to the difficulty of collecting temporally changing face images. In this paper, we propose a facial representation learning method using synthetic images for comparing faces, called ComFace, which is designed to capture intra-personal facial changes. For effective representation learning, ComFace aims to acquire two feature representations, i.e., inter-personal facial differences and intra-personal facial changes. The key point of our method is the use of synthetic face images to overcome the limitations of collecting real intra-personal face images. Facial representations learned by ComFace are transferred to three extensive downstream tasks for comparing faces: estimating facial expression changes, weight changes, and age changes from two face images of the same individual. Our ComFace, trained using only synthetic data, achieves comparable to or better transfer performance than general pre-training and state-of-the-art representation learning methods trained using real images.

翻译：日常监测与健康状况及情绪状态相关的个体内部面部变化，在医疗、健康护理及情绪识别领域具有巨大的应用潜力。然而，由于难以收集随时间变化的人脸图像，捕捉个体内部面部变化的方法尚未得到充分探索。本文提出一种使用合成图像进行人脸对比的面部表示学习方法，称为ComFace，该方法旨在捕捉个体内部的面部变化。为实现有效的表示学习，ComFace致力于获取两种特征表示，即个体间面部差异与个体内部面部变化。本方法的关键在于利用合成人脸图像以克服收集真实个体内部人脸图像的局限性。通过ComFace学习到的面部表示被迁移至三个广泛的下游人脸对比任务中：从同一个体的两幅人脸图像中估计面部表情变化、体重变化及年龄变化。仅使用合成数据训练的ComFace，在迁移性能上达到甚至超越了使用真实图像训练的通用预训练方法及最先进的表示学习方法。