Exploring the Influence of Facial Features Beyond the Eyes on Gaze Estimation

Bachelor thesis (2023)

Authors

M.T. Nguyen Manh Tan Electrical Engineering, Mathematics and Computer Science

Contributors

G. Lan Embedded Systems - EEMCS (supervisor 1)
L. Du Embedded Systems - EEMCS (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Tan Nguyen
Gaze Estimation Deep Learning Facial Contribution
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Gaze estimation holds significant importance in various applications. Pioneering research has demonstrated state-of-the-art performance in gaze estimation models by utilizing deep Convolutional Neural Networks (CNNs) and incorporating full facial images as input, instead of or in addition to solely using one or both eye images. Facial images encode crucial cues that can enhance the accuracy of gaze regression models. However, it remains unclear which specific facial features contribute and to what extent they contribute to the overall estimation accuracy. In this research, we aim to shed light on identifying the influential facial regions and quantifying their contributions to gaze estimation accuracy.