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

Title

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

 Author

Nguyen, Tan (TU Delft Electrical Engineering, Mathematics and Computer Science)

Contributor

Lan, G. (mentor)
Du, L. (mentor)

Degree granting institution

Delft University of Technology

 Programme

Computer Science and Engineering

 Project

CSE3000 Research Project

 Date

2023-06-28

 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.

 Subject

Gaze Estimation
Deep Learning
Facial Contribution

 To reference this document use:

http://resolver.tudelft.nl/uuid:df5c46b3-c9ec-4c88-8f73-32c03526be38

 Part of collection

Student theses

 Document type

bachelor thesis

 Rights

© 2023 Tan Nguyen
Files
PDF TAN_CSE3000_Final_Paper_T ... _Delft.pdf 21.75 MB
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