Abstract
In this contribution we extend existing methods for head pose estimation and investigate the use of local image phase for gaze detection. Moreover we describe how a small database of face images with given ground truth for head pose and gaze direction was acquired. With this database we compare two different computational approaches for extracting the head pose. We demonstrate that a simple implementation of the proposed methods without extensive training sessions or calibration is sufficient to accurately detect the head pose for human-computer interaction. Furthermore, we propose how eye gaze can be extracted based on the outcome of local filter responses and the detected head pose. In all, we present a framework where different approaches are combined to a single system for extracting information about the attentional state of a person.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Baluja, S., Pomerleau, D.: Non-intrusive gaze tracking using artificial neural networks. Technical Report CMU-CS-94-102, Carnegie Mellon University (1994)
Emery, N.: The eyes have it: the neuroethology, function and evolution of social gaze. Neuroscience and Biobehavioral Reviews 24, 581–604 (2000)
Eyelink (2006), http://www.eyelinkinfo.com
Gabor, D.: Theory of communication. Journal of IEE 93, 457–492 (1946)
Gee, A.H., Cipolla, R.: Determining the gaze of faces in images. Image and Vision Computing 12(10), 639–647 (1994)
Gibson, J.J., Pick, A.D.: Perception of another persons looking behaviour. American Journal of Psychology 76, 386–394 (1963)
Heinzmann, J., Zelinsky, A.: 3-d facial pose and gaze point estimation using a robust real-time tracking paradigma. In: Intern. Conf. on Automatic Face and Gesture Recognition (1998)
Hubel, D., Wiesel, T.: Receptive fields and functional architecture of monkey striate cortex. Journal of Psychology 195, 215–243 (1968)
Hutchinson, T., White Jr., K., Reichert, K., Frey, L.: Human-computer interaction using eyegaze input. IEEE Transactions on Systems, Man, and Cybernetics 19, 1527–1533 (1989)
Ji, Q., Zhu, W.: Non-intrusive eye gaze tracking for natural human computer interaction. MMI-Interactive 6 (2003)
Krüger, N., Pötzsch, M., von der Malsburg, C.: Determination of face position and pose with a learned representation based on labelled graphs. Image Vision Comput. 15(8), 665–673 (1997)
Langton, S.R., Honeyman, H., Tessler, E.: The influence of head contour and nose angle on the perception of eye-gaze direction. Perception & Psychophysics 66(5), 752–771 (2004)
Langton, S.R., Watt, R., Bruce, V.: Do the eyes have it? cues to the direction of social attention. Trends in Cognitive Science 4(2), 50–59 (2000)
Matsumoto, Y., Zelinsky, A.: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. In: 4th Intern. Conf. on Face and Gesture Recognition, pp. 499–505 (2000)
Phillips, P., Moon, H., Rauss, P., Rizvi, S.: The feret evaluation methodology for face recognition algorythems. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Rae, R., Ritter, H.: Recognition of human head orientation based on artificial neural networks. IEEE Transaction on Neural Networks 9(2), 257–265 (1998)
Sim, S., Baker, S., Bsat, M.: The cmu pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)
Sinha, P.: Last but not least. here’s looking at you, kid. Perception 29, 1005–1008 (2000)
Steifelhagen, R., Yang, J., Waibel, A.: Tracking eyes and monitoring eye gaze. In: Proc. of the Workshop on Perceptual User Interfaces, pp. 98–100 (1997)
Trucco, E., Verri, A.: Introductory Techniques for 3-D Computer Vision. Prentice Hall, Englewood Cliffs (1998)
Wang, K., Wang, Y., Yin, B., Kong, D.: Face pose estimation with a knowledge based model. In: IEEE Int. Conf. Neural Networks and Signal Processing, pp. 1131–1134 (2003)
Yoo, D.H., Chung, M.J.: A novel non-intrusive eye gaze estimation using cross-ration under large head motion. Computer Vision and Image Understanding 98, 25–51 (2005)
Zhu, Z., Ji, Q.: Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Computer Vision and Image Understanding 98, 124–154 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weidenbacher, U., Layher, G., Bayerl, P., Neumann, H. (2006). Detection of Head Pose and Gaze Direction for Human-Computer Interaction. In: André, E., Dybkjær, L., Minker, W., Neumann, H., Weber, M. (eds) Perception and Interactive Technologies. PIT 2006. Lecture Notes in Computer Science(), vol 4021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768029_2
Download citation
DOI: https://doi.org/10.1007/11768029_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34743-9
Online ISBN: 978-3-540-34744-6
eBook Packages: Computer ScienceComputer Science (R0)