  This Project was my Undergraduate Final Year dissertation, supervised by
Dimitrios Kollias This research delves into the realm of affective computing
for image analysis, aiming to enhance the efficiency and effectiveness of
multi-task learning in the context of emotion recognition. This project
investigates two primary approaches: uni-task solutions and a multi-task
approach to the same problems. Each approach undergoes testing, exploring
various formulations, variations, and initialization strategies to come up with
the best configuration. The project utilizes existing a neural network
architecture, adapting it for multi-task learning by modifying output layers
and loss functions. Tasks encompass 7 basic emotion recognition, action unit
detection, and valence-arousal estimation. Comparative analyses involve
uni-task models for each individual task, facilitating the assessment of
multi-task model performance. Variations within each approach, including, loss
functions, and hyperparameter tuning, undergo evaluation. The impact of
different initialization strategies and pre-training techniques on model
convergence and accuracy is explored. The research aspires to contribute to the
burgeoning field of affective computing, with applications spanning healthcare,
marketing, and human-computer interaction. By systematically exploring
multi-task learning formulations, this research aims to contribute to the
development of more accurate and efficient models for recognizing and
understanding emotions in images. The findings hold promise for applications in
diverse industries, paving the way for advancements in affective computing
