Facial Emotion Recognition on FER-2013 using an EfficientNetB2-Based Approach

TL;DR

This paper presents a lightweight EfficientNetB2-based model for facial emotion recognition on FER-2013, achieving high accuracy with fewer parameters and demonstrating suitability for real-time applications.

Contribution

The authors develop an efficient facial emotion recognition pipeline using EfficientNetB2, incorporating advanced training strategies to improve accuracy and reduce computational costs.

Findings

Achieved 68.78% test accuracy on FER-2013.

Model has nearly ten times fewer parameters than VGG16-based models.

Demonstrated stable training and strong generalization for real-time use.

Abstract

Detection of human emotions based on facial images in real-world scenarios is a difficult task due to low image quality, variations in lighting, pose changes, background distractions, small inter-class variations, noisy crowd-sourced labels, and severe class imbalance, as observed in the FER-2013 dataset of 48x48 grayscale images. Although recent approaches using large CNNs such as VGG and ResNet achieve reasonable accuracy, they are computationally expensive and memory-intensive, limiting their practicality for real-time applications. We address these challenges using a lightweight and efficient facial emotion recognition pipeline based on EfficientNetB2, trained using a two-stage warm-up and fine-tuning strategy. The model is enhanced with AdamW optimization, decoupled weight decay, label smoothing (epsilon = 0.06) to reduce annotation noise, and clipped class weights to mitigate class imbalance, along with dropout, mixed-precision training, and extensive real-time data augmentation. The model is trained using a stratified 87.5%/12.5% train-validation split while keeping the official test set intact, achieving a test accuracy of 68.78% with nearly ten times fewer parameters than VGG16-based baselines. Experimental results, including per-class metrics and learning dynamics, demonstrate stable training and strong generalization, making the proposed approach suitable for real-time and edge-based applications.