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Thaliath, Noel Viji (2024) A Comparative Analysis for Recognizing Emotions from Facial Expressions. Masters thesis, Dublin, National College of Ireland.
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Abstract
Emotions, categorized into anger, disgust, fear, gladness, neutrality, sadness, and surprise, significantly influence judgments and discussions on various issues. Deep learning, an artificial intelligence technique, can mimic the human brain’s data analysis to identify patterns for judgments. It uses networks to comprehend unsupervised, unstructured or unlabeled data, surpassing machine learning when dealing with large amounts of data. Unlike traditional programs, which examine data in a linear fashion, deep learning systems use a hierarchical function to handle data in a nonlinear fashion. For this research, the models developed for experimentation is a CNN model, a hybrid CNN-LSTM model, and VGG-16 model. The overall performed model was the hybrid CNN-LSTM model which gave an accuracy of 42% for a balanced dataset and 62% for unbalanced dataset. The model that performed best with a high accuracy was the CNN model that gave up to 62% in testing but gave a very high 99% accuracy during its training phase.
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