Fast and Accurate Face Orientation Measurement in Low-resolution Images on Embedded Hardware

Dries Hulens, Kristof Van Beeck, Toon Goedemé

Abstract

In a lot of applications it is important to collect some information about the gaze orientation or head-angle of a person. Just consider measuring the alertness of a car driver to see if he is still awake, or the attentiveness of people crossing a street to see if they noticed the cars driving by. In our own application we want to apply cinematographic rules (e.g. the rule of thirds where a face should be positioned left or right in the frame depending on the gaze direction) on images taken from on a UAV. Nowadays these applications should run on embedded hardware so they can be easily attached on e.g. a car or a UAV. This implies that the head angle detection algorithm should run in real-time on minimal hardware. Therefore we developed two approaches that run in real-time on embedded hardware while gaining excellent performance. We demonstrated these approaches on both a publicly available face dataset and our own dataset recorded by a UAV.

References

  1. Benfold, B. and Reid, I. (2008). Colour invariant head pose classification in low resolution video. InBMVC, pages 1-10.
  2. Benfold, B. and Reid, I. (2009). Guiding visual surveillance by tracking human attention. In BMVC, pages 1-11.
  3. Fanelli, G., Gall, J., and Van Gool, L. (2011). Real time head pose estimation with random regression forests. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 617-624. IEEE.
  4. Fanelli, G., Gall, J., and Van Gool, L. (2012). Real time 3d head pose estimation: Recent achievements and future challenges. In Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on, pages 1-4. IEEE.
  5. Gourier, N., Hall, D., and Crowley, J. L. (2004). Estimating face orientation from robust detection of salient facial structures. In FG Net Workshop on Visual Observation of Deictic Gestures, pages 1-9. FGnet (IST2000-26434) Cambridge, UK.
  6. Hulens, D., Goedemé, T., and Rumes, T. (2014). Autonomous lecture recording with a ptz camera while complying with cinematographic rules. In Computer and Robot Vision (CRV), 2014 Canadian Conference on, pages 371-377. IEEE.
  7. Liew, C. F. and Yairi, T. (2015). Human head pose estimation and its application in unmanned aerial vehicle control. In The Malaysia-Japan Model on Technology Partnership, pages 327-336. Springer.
  8. Liu, Y., Wang, Q., Jiang, Y., and Lei, Y. (2014). Supervised locality discriminant manifold learning for head pose estimation. Knowledge-Based Systems, 66:126-135.
  9. Lu, J. and Tan, Y.-P. (2013). Ordinary preserving manifold analysis for human age and head pose estimation. Human-Machine Systems, IEEE Transactions on, 43(2):249-258.
  10. Marks, T. and Jones, M. (2015). Real-time head pose estimation and facial feature localization using a depth sensor and triangular surface patch features.
  11. Oyini Mbouna, R., Kong, S. G., and Chun, M.-G. (2013). Visual analysis of eye state and head pose for driver alertness monitoring. Intelligent Transportation Systems, IEEE Transactions on, 14(3):1462-1469.
  12. Paone, J., Bolme, D., Ferrell, R., Aykac, D., and Karnowski, T. (2015). Baseline face detection, head pose estimation, and coarse direction detection for facial data in the shrp2 naturalistic driving study. In Intelligent Vehicles Symposium (IV), 2015 IEEE, pages 174-179. IEEE.
  13. Pyun, N.-J., Sayah, H., and Vincent, N. (2014). Adaptive haar-like features for head pose estimation. In Image Analysis and Recognition, pages 94-101. Springer.
  14. Rehder, E., Kloeden, H., and Stiller, C. (2014). Head detection and orientation estimation for pedestrian safety. In Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on, pages 2292- 2297. IEEE.
  15. Schulz, A. and Stiefelhagen, R. (2012). Video-based pedestrian head pose estimation for risk assessment. In Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, pages 1771-1776. IEEE.
  16. Shbib, R., Zhou, S., Ndzi, D., and Alkadhimi, K. (2014). Head pose estimation for car drivers. International Journal of u-and e-Service, Science and Technology, 7(4):359-374.
  17. Tawari, A., Martin, S., and Trivedi, M. M. (2014). Continuous head movement estimator for driver assistance: Issues, algorithms, and on-road evaluations. Intelligent Transportation Systems, IEEE Transactions on, 15(2):818-830.
  18. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I-511. IEEE.
  19. Yano, S., Gu, Y., and Kamijo, S. (2014). Estimation of pedestrian pose and orientation using on-board camera with histograms of oriented gradients features. International Journal of Intelligent Transportation Systems Research, pages 1-10.
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Paper Citation


in Harvard Style

Hulens D., Van Beeck K. and Goedemé T. (2016). Fast and Accurate Face Orientation Measurement in Low-resolution Images on Embedded Hardware . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 538-544. DOI: 10.5220/0005716105380544


in Bibtex Style

@conference{visapp16,
author={Dries Hulens and Kristof Van Beeck and Toon Goedemé},
title={Fast and Accurate Face Orientation Measurement in Low-resolution Images on Embedded Hardware},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={538-544},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005716105380544},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Fast and Accurate Face Orientation Measurement in Low-resolution Images on Embedded Hardware
SN - 978-989-758-175-5
AU - Hulens D.
AU - Van Beeck K.
AU - Goedemé T.
PY - 2016
SP - 538
EP - 544
DO - 10.5220/0005716105380544