An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement

By Y. Matsumoto, A. Zelinsky. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), 2000

325 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/8ab18ccb8796ae5d805a32640ba1c43261fc2419

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Real Time Head Pose Estimation from Consumer Depth Cameras (2011, background): “Feature-based methods are limited by their need to either have the same facial features visible across different poses, or define pose-dependent features [24, 16].”
Combining Head Pose and Eye Location Information for Gaze Estimation (2012, unknown): “Newman and Matsumoto [28] and Matsumoto et al. [32] consider a tracking scenario equipped with stereo cameras and employ 2-”
3D Face Pose Tracking using Low Quality Depth Cameras (2013, methodology): “While Appearance-based methods attempt to use holistic facial appearance (Morency et al., 2003), Feature-based methods rely on the localization of specific facial features and suppose that some of these are visible in all poses (Yang and Zhang, 2002; Matsumoto and Zelinsky, 2000).”
Robots that imitate humans. (2002, background): “Many machine vision systems have looked at the problems of identifying cues that indicate attention, such as pointing [28], head pose [29], or gaze direction [ 30 ].”
Determining driver visual attention with one camera (2003, methodology): “In [24] a method is presented which tracks the head and estimates pose.”
General theory of remote gaze estimation using the pupil center and corneal reflections (2006, background): “One approach consists of tracking facial features to estimate the 3-D head pose and thus derive the positions of the center of rotation of the eyes [16], [17].”
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze (2010, unknown): “…detection techniques consists of shape-based [138], [47], [68], [166], [86], [167], [71], [36], [37], [36], [111], [44], [57], [76], [75], [78], [77], [138], [149], [120], [129] [130], appearance-based [117], [102], [61], [82], [148], [35], and hybrid methods [67], [46], [98], [158], [50], [169].”
Real time head pose estimation with random regression forests (2011, methodology): “, [26, 29, 16], or use pose-dependent features; for example, Yao and Cham [30] select feature points manually and match them to a generic wireframe model.”