Deep Learning-Based Emotion Recognition with Genetic Optimizer

Introduction

This repository presents research on emotion recognition from facial expressions using ResNet50 and VGG16 models, combined with a genetic optimizer. The work investigates the effectiveness of deep learning in interpreting emotions, highlighting the potential of these models while addressing the computational difficulties associated with large datasets and complex architectures.

Research Paper

The comprehensive research paper, available in this repository: Facial_Emotions_Recognition_Paper.docm, covers the following key topics:

• Model Application:

  • Evaluation of ResNet50 and VGG16 for emotion detection.

• Genetic Optimization:

  • Implementation of a genetic optimizer to improve model accuracy.

• Computational Challenges:

  • Analysis of the challenges posed by large datasets and intricate model structures.

• Training Time Constraints:

  • Insights into the time limitations encountered during model training.

• Practical Constraints:

  • Discussion on the practical limitations of deep learning models when computational resources are restricted.

• Summary:

  • Highlighting the potential of deep learning and genetic optimization in detecting subtle emotional signals despite computational hurdles.

Final Thoughts and Future Work

The research underscores the importance of using advanced deep learning models for emotion recognition. It also emphasizes the need to tackle computational challenges and proposes future research directions, such as:

• Improving Training Efficiency:

  • Investigating approaches like distributed computing or upgrading hardware to reduce the time required for processing large datasets.

• Enhancing Genetic Optimization:

  • Refining genetic optimization methods to create real-time emotion recognition systems that achieve a balance between accuracy and efficiency.