A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models

TL;DR

This study compares CNN and Modified VGG16 models for emotion recognition across two datasets, highlighting the impact of dataset diversity on model performance and suggesting future research directions.

Contribution

It provides a comparative analysis of CNN and Modified VGG16 models for emotion recognition on FER2013 and AffectNet datasets, emphasizing dataset diversity's importance.

Findings

Modified VGG16 outperforms CNN in accuracy.

Performance drops on AffectNet for both models.

Dataset diversity significantly affects emotion recognition results.

Abstract

Emotion recognition is a critical aspect of human interaction. This topic garnered significant attention in the field of artificial intelligence. In this study, we investigate the performance of convolutional neural network (CNN) and Modified VGG16 models for emotion recognition tasks across two datasets: FER2013 and AffectNet. Our aim is to measure the effectiveness of these models in identifying emotions and their ability to generalize to different and broader datasets. Our findings reveal that both models achieve reasonable performance on the FER2013 dataset, with the Modified VGG16 model demonstrating slightly increased accuracy. When evaluated on the Affect-Net dataset, performance declines for both models, with the Modified VGG16 model continuing to outperform the CNN. Our study emphasizes the importance of dataset diversity in emotion recognition and discusses open problems and future research directions, including the exploration of multi-modal approaches and the development of more comprehensive datasets.