Study on Eye Gaze Detection Using Deep Transfer Learning Approaches

Many applications, including security systems, medical diagnostics, and human-computer interfaces, depend on eye gaze recognition. However, due to factors including individual variations, occlusions, and shifting illumination conditions, real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition. This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency. Transfer learning is the process of fine-tuning pre-trained models on smaller, domain-specific datasets after they have been trained on larger datasets. We study several transfer learning algorithms and evaluate their effectiveness on eye gaze identification, including both Regression and Classification tasks, using a range of deep learning architectures, namely AlexNet, Visual Geometry Group (VGG), InceptionV3, and ResNet. In this study, we evaluate the effectiveness of transfer learning-based models against models that were trained from scratch using eye-gazing datasets on grounds of various performance and loss metrics such as Precision, Accuracy, and Mean Absolute Error. We investigate the effects of different pre-trained models, dataset sizes, and domain gaps on the transfer learning process, and the findings of our study clarify the efficacy of transfer learning for eye gaze detection and offer suggestions for the most successful transfer learning strategies to apply in real-world situations.