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

In this paper, we study the task of facial expression recognition under strong occlusion. We are particularly interested in cases where 50 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.

READ FULL TEXT

page 1

page 7

research
11/20/2021

Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion

We study a series of recognition tasks in two realistic scenarios requir...
research
05/25/2023

Triplet Knowledge Distillation

In Knowledge Distillation, the teacher is generally much larger than the...
research
11/12/2019

Recognizing Facial Expressions of Occluded Faces using Convolutional Neural Networks

In this paper, we present an approach based on convolutional neural netw...
research
03/29/2021

Complementary Relation Contrastive Distillation

Knowledge distillation aims to transfer representation ability from a te...
research
04/19/2019

Knowledge Distillation via Route Constrained Optimization

Distillation-based learning boosts the performance of the miniaturized n...
research
11/25/2018

Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation

Typically, the deployment of face recognition models in the wild needs t...
research
09/23/2022

Descriptor Distillation: a Teacher-Student-Regularized Framework for Learning Local Descriptors

Learning a fast and discriminative patch descriptor is a challenging top...

Please sign up or login with your details

Forgot password? Click here to reset