Self-supervised Learning for Fine-grained Ethnicity Classification under Limited Labeled Data

Abstract

Human faces are always determined by genes and other external causes, such as geographical environment, which makes it possible for us to predict ethnicity according to the faces. However, it remains a challenging task due to the tiny differences in faces for various ethnicities, which is hard for human beings to tell, especially for ethnicities on the same continent, e.g., East Asia. Although some strongly-supervised methods have demonstrated their feasibility in this task, they cease to be effective when suffering from data-hungry issues in practice. This paper proposes a novel self-supervised model with a polynomial stacked attention mechanism to well excavate distinctions across different nations under limited labeled data. And we also construct a new ethnicity dataset named Cupid which observably extends the scale and categories of ethnic data compared to the existing datasets. Extensive experiments confirm that our method achieves the state-of-the-art results on both the Asian Face dataset and our proposed Cupid dataset.