Real-time Facial Emotion Recognition Web Application

Abstract

This thesis addresses the challenge of real-time facial emotion recognition through the lens of Deep Convolutional Neural Networks (DCNNs). While DCNNs have proven to be state-ofthe-art for image classification, their efficacy is contingent upon abundant, accurately labeled data. Emotion recognition, a complex and subjective task, suffers from a scarcity of comprehensive datasets. Existing facial expression databases lack the depth required to train deep neural networks effectively, particularly those that have excelled in object recognition tasks. The limitations are further compounded by high inter-subject variations, including age, gender, ethnic backgrounds, and individual expressiveness levels. Facial images are also subject to occlusions, pose variations, and wearable accessories, intensifying the complexity of the emotion recognition problem. Consequently, the choice of a benchmark dataset plays a pivotal role in the development and evaluation of an effective model. In this research, we explore the nuances of real-time facial emotion recognition, delving into the challenges posed by limited and diverse datasets. Our approach involves a deep investigation into the intricacies of emotion representation in facial images, leveraging the power of DCNNs. By addressing the dataset limitations and exploring techniques to mitigate the impact of diverse personal attributes, we aim to contribute to the advancement of facial emotion recognition models. The ultimate goal is the development of a real-time facial emotion recognition web application, demonstrating the practical application of our findings in a user-friendly and accessible manner