Self-supervised learning of a facial attribute embedding from video

Understand

We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time.

• To perform this task, we introduce a network, Facial Attributes-Net (FAb-Net), that is trained to embed multiple frames from the same video face-track into a common low-dimensional space.
• With this approach, we make three contributions: first, we show that the network can leverage information from multiple source frames by predicting confidence/attention masks for each frame; second, we demonstrate that using a curriculum learning regime improves the learned embedding; finally, we demonstrate that the network learns a meaningful face embedding that encodes information about head pose, facial landmarks and facial expression, i.e.
• facial attributes, without having been supervised with any labelled data.