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Support Vector Machine-Based Face Direction Detection Using an Infrared Array Sensor

  • Zhangjie Chen
  • Hanwei Liu
  • Ya Wang
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Facing direction detection plays a critical role in human computer interaction, such as face recognition and head pose estimation in biometric identification, spatial microphone/loudspeaker devices, virtual reality games and etc. Currently detection methods are mainly focused on extracting specific patterns of various facial features from the user’s optical images, which raises concerns on privacy invasion and these detection techniques do not usually work in the dark environment. To address these concerns, this paper proposes a pervasive solution for a coarse facing direction detection using a low pixel infrared thermopile array sensor. Support vector machine (SVM) method is selected as the classifier. Two methods for feature extraction are compared. One is based on pre-defined features and the other is based on pre-trained convolutional neural network (CNN) model. The detection accuracy resulted from using pre-defined features reaches 87% for identifying five different facing directions up to 1.2 m. However, this accuracy is largely descended when the detection range is increased to 1.8 m. Interestingly, the accuracy resulted from using pre-trained CNN features, however, demonstrates a reliable and steady performance up to 1.8 m. The accuracy keeps above 95% at both detection ranges (1.2 and 1.8 m). This proposed solution leads to many advantages, such as low resolution leading to no intention on privacy invasion, and the low-cost intriguing a potentially large market for human computer interaction in smart home appliances control and computer games.

Keywords

Infrared array sensor Facing direction SVM CNN Human computer interaction 

References

  1. 1.
    Al-Rahayfeh, A., Faezipour, M.: Eye tracking and head movement detection: a state-of-art survey. IEEE J. Transl. Eng. Health Med. 1, 12 (2013)CrossRefGoogle Scholar
  2. 2.
    Xie, D., Dang, L., Tong, R.: Video based head detection and tracking surveillance system. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE (2012)Google Scholar
  3. 3.
    Yim, J., Qiu, E., Graham, T.: Experience in the design and development of a game based on head-tracking input. In: Proceedings of the 2008 Conference on Future Play: Research, Play, Share, ACM (2008)Google Scholar
  4. 4.
    Liu, K., et al.: Attention recognition of drivers based on head pose estimation. In: Vehicle Power and Propulsion Conference, VPPC’08, IEEE (2008)Google Scholar
  5. 5.
    Sasou, A.: Acoustic head orientation estimation applied to powered wheelchair control. In: Second International Conference on Robot Communication and Coordination, ROBOCOMM’09, IEEE (2009)Google Scholar
  6. 6.
    Miyanokoshi, Y., et al.: Suspicious behavior detection based on case-based reasoning using face direction. In: 2006 SICE-ICASE International Joint Conference, pp. 5429–5432 (2006)Google Scholar
  7. 7.
    Ugurlu, Y.: Head posture detection using skin and hair information. In: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE (2012)Google Scholar
  8. 8.
    Al-Rahayfeh, A., Faezipour, M., IEEE: Enhanced eye gaze direction classification using a combination of face detection, CHT and SVM. In: 2013 IEEE Signal Processing in Medicine and Biology Symposium (2013)Google Scholar
  9. 9.
    Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation and augmented reality tracking: an integrated system and evaluation for monitoring driver awareness. IEEE Trans. Intell. Transp. Syst. 11(2), 300–311 (2010)CrossRefGoogle Scholar
  10. 10.
    Paone, J., et al.: Baseline face detection, head pose estimation, and coarse direction detection for facial data in the SHRP2 naturalistic driving study. In: 2015 IEEE Intelligent Vehicles Symposium, pp. 174–179. IEEE, New York (2015)CrossRefGoogle Scholar
  11. 11.
    Manogna, S., Vaishnavi, S., Geethanjali, B.: Head movement based assist system for physically challenged. In: 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), IEEE (2010)Google Scholar
  12. 12.
    Kim, S., et al.: Head mouse system based on gyro-and opto-sensors. In: 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI), IEEE (2010)Google Scholar
  13. 13.
    O’Regan, S., Marnane, W.: Multimodal detection of head-movement artefacts in EEG. J. Neurosci. Methods. 218(1), 110–120 (2013)CrossRefGoogle Scholar
  14. 14.
    Tyndall, A., Cardell-Oliver, R., Keating, A.: Occupancy estimation using a low-pixel count thermal imager. IEEE Sensors J. 16(10), 3784–3791 (2016)CrossRefGoogle Scholar
  15. 15.
    Yun, J., Song, M.: Detecting direction of movement using pyroelectric infrared sensors. IEEE Sensors J. 14(5), 1482–1489 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Vedaldi, A., Zisserman, A.: VGG Convolutional Neural Networks Practical. Available from: http://www.robots.ox.ac.uk/~vgg/practicals/cnn/#vgg-convolutional-neural-networks-practical (2016)

Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringStony Brook UniversityStony BrookUSA
  2. 2.Department of Electrical EngineeringStony Brook UniversityStony BrookUSA

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