Improving Facial Expression Classification through Ensemble Deep Learning Models

This article presents a comprehensive study on improving facial expression classification through the deployment of an ensemble deep learning model that amalgamates multiple advanced CNN architectures, including Inception V3 (Inception Architecture Version-3), ResNet50 (Residual Network with 50 layers), and SPP-net (Spatial Pyramid Pooling in Deep Convolutional Networks).By leveraging the strengths of these diverse models, the ensemble approach aims to capture a richer representation of facial features, thereby enhancing classification accuracy.The study meticulously outlines the data preprocessing techniques employed, such as image segmentation and normalization, to prepare the AffectNet dataset for model training and evaluation.On the AffectNet dataset, the proposed ensemble model achieves 85.2% classification accuracy, outperforming the best individual CNN by 5-10%.This marks a significant improvement, showcasing the potential of ensemble methods in the field of facial expression recognition.The article highlights future research directions, including the exploration of end-toend trainable ensemble models and the collection of more diverse datasets to further refine and enhance model performance.This study contributes to the ongoing advancements in affective computing by demonstrating the effectiveness of ensemble deep-learning models in improving facial expression classification accuracy.It opens new avenues for research and application, promising to enrich human-computer interactions and deepen our understanding of human emotions.