Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T10:12:24.742Z Has data issue: false hasContentIssue false
  • English
  • Français

Design and Implementation of a Facial Character Analysis Algorithm for Humanoid Robots

Published online by Cambridge University Press:  10 April 2019

Fatma Göngör  and
Önder Tutsoy
Fatma Göngör
Affiliation:
Electrical and Electronic Engineering Department, Adana Science and Technology University, Adana, Turkey. E-mail: [email protected]
Önder Tutsoy*
Affiliation:
Electrical and Electronic Engineering Department, Adana Science and Technology University, Adana, Turkey. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Humanoid robots (HR) equipped with a sophisticated facial character analysis (FCA) algorithm can able to initiate crucial improvements in human–robot interactions. This paper, for the first time in the literature, proposes a three-stage FCA algorithm for the HR. At the initial stage of this algorithm, the HR detects the face with the Viola–Jones algorithm, and then important facial distance measurements are obtained with the geometric-based facial distance measurement technique. Finally, the measured facial distances are evaluated with the physiognomy science to reveal the characteristic properties of a person. Even though the proposed algorithm can be implemented to all HR, in this paper, it has been specifically applied to NAO HR. The reliability of the developed FCA algorithm is verified by analyzing each terminal decision about the character and its connection with the measured facial distances in the anatomy science.

Keywords

Geometric-based facial distance measurement techniqueFacial character analysisFeature extractionHuman–robot interactionNAO humanoid robotphysiognomy scienceViola–Jones algorithm
Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1850 - 1866
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Viola, P. and Jones, M. J., “Robust real-time face detection,” Int. J. Comput Vis. 57(2), 137–154 (2004).CrossRefGoogle Scholar
Jensen, O. H., Implementing the Viola-Jones Face Detection Algorithm, Master’s thesis (Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark, 2008).Google Scholar
Schneiderman, H. and Kanade, T., A Statistical Method for 3D Object Detection Applied to Faces and Cars. CVPR (IEEE Computer Society, Washington, D.C., USA, 2000). ISBN: 0-7695-0662-3.Google Scholar
Rowley, H. A., Baluja, S. and Kanade, T., “Neural network-based face detection,” IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–38 (1998).CrossRefGoogle Scholar
Kanade, T., Picture Processing System by Computer Complex and Recognition of Human Faces (Kyoto University, Kyoto, 1974).Google Scholar
Schapire, R. E., “Explaining AdaBoost,” In: Empirical Inference (Schölkopf, B., Luo, Z. and Vovk, V. eds.) (Springer, Berlin, Heidelberg, 2013) pp. 37–52.CrossRefGoogle Scholar
Brunelli, R. and Poggio, T., “Face recognition: Features versus templates,” IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993).CrossRefGoogle Scholar
Lavater, J. C., Essays on Physiognomy (T. Holcroft, Trans.) (William Tegg, London, England, 1869). (Original work published 1772).Google Scholar
Wells, R. D. B., Faces we Meet and How to Read Them (Vickers, London, 1870) p. 14.Google Scholar
Gonsior, B., Sosnowski, S., Mayer, C., Blume, J., Radig, B., Wollherr, D. and Kühnlenz, K., “Improving aspects of empathy subjective performance for HRI through mirroring emotions,” Proceedings of IEEE Intern. Symposium on Robot and Human Interactive Communication, RO-MAN 2011, Atlanta, GA, USA (2011).CrossRefGoogle Scholar
Valstar, M. and Pantic, M., “Fully automatic facial action unit detection and temporal analysis,” Proceedings of IEEE Computer Vision and Pattern Recognition Workshop, New York, NY, USA (2006) pp. 149.Google Scholar
Cox, I. J., Ghosn, J. and Yianilos, P. N., “Feature-based face recognition using mixture-distance,”. In: Proceedings CVPR’96, 1996 IEEE Computer Society Conference Computer Vision and Pattern Recognition, San Francisco, CA, USA (1996, June) pp. 209–216.Google Scholar
Barasch, M., “Character and physiognomy: Bocchi on Donatello’s St. George a Renaissance text on expression in Art,” J. Hist Ideas 36(3), 413–430 (1975).CrossRefGoogle Scholar
Oommen, A. and Oommen, T., “Physiognomy: A critical review,” J Anat. Soc. India 52(2), 189–191 (2003).Google Scholar
Angle, S., Bhagtani, R. and Chheda, H., “Biometrics: A further echelon of security,” In: UAE International Conference on Biological and Medical Physics, Dubai, United Arab Emirates (2005, March).Google Scholar
Charles, E., Major Psychological Assessment Instruments (Allyn & Bacon, Boston, MA, USA, 1996).Google Scholar
Mong, L. S., The Chinese Art of Studying the Head, Face and Hands (Eagle Trading Sdn Bhd, Malaysia, 1989).Google Scholar
Brunelli, R. and Poggio, T., “Face recognition: Features versus templates,” IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993).CrossRefGoogle Scholar