Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Authors

Yuanfeng Li
Jiangang Deng
Qun Wu
Ying Wang

DOI

10.9781/ijimai.2021.06.002

Abstract

Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General

Source Publication

International Journal of Interactive Multimedia and Artificial Intelligence

Recommended Citation

Li, Yuanfeng; Deng, Jiangang; Wu, Qun; and Wang, Ying (2021) "Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks," International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 7: Iss. 2, Article 18.
DOI: 10.9781/ijimai.2021.06.002
Available at: https://ijimai.researchcommons.org/ijimai/vol7/iss2/18