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

• URL: http://arxiv.org/abs/2008.01003v2
• Date: Thu, 25 Feb 2021 18:54:30 GMT
• Title: Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion
• Authors: Mariana-Iuliana Georgescu, Radu Tudor Ionescu
• Abstract summary: We are interested in cases where 50% of the face is occluded, e.g. when the subject wears a Virtual Reality (VR) headset. Previous studies show that pre-training convolutional neural networks (CNNs) on fully-visible faces improves the accuracy. We propose to employ knowledge distillation to achieve further improvements.
• Score: 29.639941810500638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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