Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion

TL;DR

This paper introduces a novel knowledge distillation approach using triplet loss for facial expression recognition under occlusion, significantly improving accuracy on challenging VR scenarios.

Contribution

It proposes a new triplet loss-based knowledge distillation method and combines it with classic teacher-student training for better occluded face recognition.

Findings

Significant accuracy improvements on FER+ and AffectNet datasets.

Effective handling of 50% occlusion in facial expression recognition.

Outperforms existing state-of-the-art methods for occluded faces.

Abstract

In this paper, we study the task of facial expression recognition under strong occlusion. We are particularly interested in cases where 50% of the face is occluded, e.g. when the subject wears a Virtual Reality (VR) headset. While previous studies show that pre-training convolutional neural networks (CNNs) on fully-visible (non-occluded) faces improves the accuracy, we propose to employ knowledge distillation to achieve further improvements. First of all, we employ the classic teacher-student training strategy, in which the teacher is a CNN trained on fully-visible faces and the student is a CNN trained on occluded faces. Second of all, we propose a new approach for knowledge distillation based on triplet loss. During training, the goal is to reduce the distance between an anchor embedding, produced by a student CNN that takes occluded faces as input, and a positive embedding (from the same class as the anchor), produced by a teacher CNN trained on fully-visible faces, so that it becomes smaller than the distance between the anchor and a negative embedding (from a different class than the anchor), produced by the student CNN. Third of all, we propose to combine the distilled embeddings obtained through the classic teacher-student strategy and our novel teacher-student strategy based on triplet loss into a single embedding vector. We conduct experiments on two benchmarks, FER+ and AffectNet, with two CNN architectures, VGG-f and VGG-face, showing that knowledge distillation can bring significant improvements over the state-of-the-art methods designed for occluded faces in the VR setting.