Real-Time 3D Head Pose Tracking Through 2.5D Constrained Local Models with Local Neural Fields

2019 ◽  
Vol 127 (6-7) ◽  
pp. 579-598
Author(s):  
Stephen Ackland ◽  
Francisco Chiclana ◽  
Howell Istance ◽  
Simon Coupland
Keyword(s):  
Real Time ◽  
Neural Fields ◽  
Local Models ◽  
Head Pose ◽  
Pose Tracking

Head pose tracking using GPU based real-time 3D registration

2013 ◽  
Author(s):  
Sung-In Choi ◽  
Udaya Wijenayake ◽  
Soon-Yong Park
Keyword(s):  
Real Time ◽  
Head Pose ◽  
3D Registration ◽  
Pose Tracking

Real-Time Head Pose Tracking with Online Face Template Reconstruction

2016 ◽  
Vol 38 (9) ◽  
pp. 1922-1928 ◽  
Author(s):  
Songnan Li ◽  
King Ngi Ngan ◽  
Raveendran Paramesran ◽  
Lu Sheng
Keyword(s):  
Real Time ◽  
Head Pose ◽  
Pose Tracking

Head Pose Tracking Method Based on Face Detection And Stereo Visual SLAM

2021 ◽  
Author(s):  
Suibin Huang ◽  
Hua Xiao ◽  
Peng Han ◽  
Jian Qiu ◽  
Li Peng ◽  
...  
Keyword(s):  
Face Detection ◽  
Visual Slam ◽  
Head Pose ◽  
Tracking Method ◽  
Pose Tracking

scholarly journals Real-time gaze estimation via pupil center tracking

2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Natural Environments ◽  
Gaze Estimation ◽  
Head Pose ◽  
Data Set ◽  
Gaze Tracking ◽  
Illumination Changes ◽  
Wide Range ◽  
Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

Sign in / Sign up

Export Citation Format

Share Document