TY - GEN
T1 - PGV
T2 - 11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024
AU - Al-Ikhsan, Hamdani
AU - Bachtiar, Fitra Abdurrachman
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Eye tracking is an important variable and offers benefits in Human-computer interaction (HCI) which is often complex and impractical in practice. Eye tracking implementation usually requires specialized devices, technology, or setup to work. On top of that, challenges like face obstructions like glasses or masks, differences in lighting, distance, and angles add another layer to the implementation difficulty. A novel random point detector algorithm called Pixel Gradient Value (PGV) is proposed which is an interest point detector-like algorithm to be used for eye-tracking purposes, which works by calculating the gradient value of a pixel in a given neighborhood by using an edge detection operator like Sobel edge detector and Canny edge detector as its basis. PGV is image-agnostic, it is capable of covering a wide amount of eye-tracking scenarios while running on a minimum setup. PGV is then used in a series of Fully Convolutional Network (FCN) machine learning system used for classification and point localization of the pupil center point. The proposed model is able to achieve higher results compared to the state-of-the-art result of em ax ≤ 0.025 value of 96.04, em ax ≤ 0.05 value of 98.71 and em ax ≤ 0.01 value of 99.19.
AB - Eye tracking is an important variable and offers benefits in Human-computer interaction (HCI) which is often complex and impractical in practice. Eye tracking implementation usually requires specialized devices, technology, or setup to work. On top of that, challenges like face obstructions like glasses or masks, differences in lighting, distance, and angles add another layer to the implementation difficulty. A novel random point detector algorithm called Pixel Gradient Value (PGV) is proposed which is an interest point detector-like algorithm to be used for eye-tracking purposes, which works by calculating the gradient value of a pixel in a given neighborhood by using an edge detection operator like Sobel edge detector and Canny edge detector as its basis. PGV is image-agnostic, it is capable of covering a wide amount of eye-tracking scenarios while running on a minimum setup. PGV is then used in a series of Fully Convolutional Network (FCN) machine learning system used for classification and point localization of the pupil center point. The proposed model is able to achieve higher results compared to the state-of-the-art result of em ax ≤ 0.025 value of 96.04, em ax ≤ 0.05 value of 98.71 and em ax ≤ 0.01 value of 99.19.
KW - computer vision
KW - eye tracking
KW - fully convolutional neural network
KW - interest point detector
UR - https://www.scopus.com/pages/publications/85214688273
U2 - 10.1109/ICITACEE62763.2024.10761950
DO - 10.1109/ICITACEE62763.2024.10761950
M3 - Conference contribution
AN - SCOPUS:85214688273
T3 - Proceedings - 11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024
SP - 59
EP - 64
BT - Proceedings - 11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024
A2 - Facta, Mochammad
A2 - Riyadi, Munawar Agus
A2 - Arfan, M.
A2 - Soetrisno, Yosua Alvin Adi
A2 - Wulandari, Alfia Putri
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 August 2024 through 30 August 2024
ER -