  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.
