Occlusion Contrasts for Self-Supervised Facial Age Estimation

Abstract

In this paper, we propose an Occlusion Contrast(OCCO) approach for self-supervised facial partial occluded age estimation. Unlike the conventional facial age estimation approaches which utilize fully-visible faces as input data that does not generalize well for occlusion images, our approach aims to ignore the occlusion and only focus on the non-occluded facial areas so that we can improve the occluded facial age estimation accuracy. To achieve this, we utilize self-supervised contrastive learning to learn non-occluded feature representation, since contrastive learning makes the distances between the anchor and positive samples as close as possible in embedded space, while simultaneously pushing apart the negative samples. Furthermore, our OCCO incorporates with ordinal relationship of different ages, which is modeled by the deep label distribution learning. Considering that face aging datasets usually undergo a label imbalance problem, we employ the cost-sensitive strategy to constrain the learning of classifier. Extensive experimental results on two face aging datasets show that our OCCO not only achieve satisfactory performance over the masked faces but also comparable to the state-of-the-art age estimation methods for raw facial images.