Boosting Facial Landmark Detection via Self-supervised and Semi-supervised Learning

Nguyen Minh Chau, Toan Nguyen Ngoc, Tuyen Le Dinh, Sang Dinh Viet, Pooi-Mun Wong, Chin-Boon Chng, Chee-Kong Chui

Abstract: Keypoint detection is one of the main focused fields in computer vision with various applications. Traditional fully-supervised deep learning methods currently dominate the field with impressive accuracy, but typically require careful, expensive, and laborious effort for keypoint annotations. To tackle this problem, recent semi-supervised methods have emerged and shown great potential in utilizing a large amount of available unlabeled data. In this work, we explore a novel semi-supervised keypoint detection method, aiming to reduce the annotations required while maintaining the accuracy of traditional fully-supervised methods. We further augment the method by integrating it with a robust backbone network that has been pre-trained through self-supervised learning, thereby enabling better utilization of unlabeled data. Experimental results on three different datasets show that models trained using our semi-supervised method outperform their fully-supervised counterparts in accuracy despite using the same amount of labeled data. Additionally, under specific settings, our method can match the performance of existing semi-supervised methods even when using a reduced set of labeled data.