Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild

TL;DR

This paper presents a deep CNN model for facial expression recognition in unconstrained environments, achieving moderate accuracy and F1 scores in a competitive benchmark setting.

Contribution

The paper introduces a novel deep CNN architecture tailored for in-the-wild facial expression recognition and evaluates its performance on the ABAW Challenge dataset.

Findings

Accuracy of 50.77% on validation set

F1 score of 29.16% on validation set

Demonstrates feasibility of CNNs for in-the-wild FER

Abstract

This paper describes the proposed methodology, data used and the results of our participation in the ChallengeTrack 2 (Expr Challenge Track) of the Affective Behavior Analysis in-the-wild (ABAW) Competition 2020. In this competition, we have used a proposed deep convolutional neural network (CNN) model to perform automatic facial expression recognition (AFER) on the given dataset. Our proposed model has achieved an accuracy of 50.77% and an F1 score of 29.16% on the validation set.