A Multi-resolution Approach to Expression Recognition in the Wild

TL;DR

This paper introduces a multi-resolution deep learning approach using ResNet-like architecture with Squeeze-and-Excitation blocks for facial expression recognition in unconstrained environments, achieving over 90% accuracy on a challenging dataset.

Contribution

It proposes a novel multi-resolution method tailored for FER that accounts for varying face image resolutions, improving recognition performance.

Findings

Achieved over 90% accuracy on Affect-in-the-Wild 2 dataset.

Demonstrated the effectiveness of multi-resolution training for FER.

Validated the approach using validation set due to lack of test set.

Abstract

Facial expressions play a fundamental role in human communication. Indeed, they typically reveal the real emotional status of people beyond the spoken language. Moreover, the comprehension of human affect based on visual patterns is a key ingredient for any human-machine interaction system and, for such reasons, the task of Facial Expression Recognition (FER) draws both scientific and industrial interest. In the recent years, Deep Learning techniques reached very high performance on FER by exploiting different architectures and learning paradigms. In such a context, we propose a multi-resolution approach to solve the FER task. We ground our intuition on the observation that often faces images are acquired at different resolutions. Thus, directly considering such property while training a model can help achieve higher performance on recognizing facial expressions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset. Not being available a test set, we conduct tests and models selection by employing the validation set only on which we achieve more than 90\% accuracy on classifying the seven expressions that the dataset comprises.