Real Time Facial Expression Recognition using EfficientNetB7 Model Architecture

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Authors
Gattu, Anwesh
Issue Date
2024
Degree
MSc in Data Analytics
Publisher
Dublin Business School
Rights
Abstract
Facial expression recognition (FER) represents a pivotal aspect of human interaction and emotional communication. This research delves into the development and implementation of a real-time facial expression recognition system using the FER2013 dataset obtained from Kaggle. Leveraging the cutting-edge EfficientNetB7 model, the study aimed to create a robust and accurate model capable of detecting various facial expressions in real-time through webcam feeds. Employing a combination of Machine Learning and Deep Learning methodologies, the project focused on training the model to categorize facial expressions into distinct emotions. The methodologies encompassed preprocessing steps, data augmentation techniques, and the design of an EfficientNet7 architecture tailored to the nuances of facial feature recognition. The model's performance was evaluated using metrics such as accuracy, precision, recall, and loss. This research encapsulates the comprehensive approach and findings achieved in the pursuit of advancing real-time facial expression recognition technology.