Understanding the Energy Saving Potential of Smart Scale Selection in the Viola and Jones Facial Detection Algorithm

Noel Perez, Sérgio Faria, Miguel Coimbra

2017

Abstract

In this paper we study the energy saving potential of smart scale selection methods when using the Viola and Jones face detector running on smartphone devices. Our motivation is that cloud and edge-cloud multi-user environments may provide enough contextual information to create this type of scale selection algorithms. Given their non-trivial design, we must first inspect its actual benefits, before committing important research resources to actually produce relevant smart scale selection methods. Our experimental methodology in this paper assumes the optimum scenario of a perfect selection of scales for each image (drawn from ground truth annotation, using well-known public datasets), comparing it with the typical multi-scale geometrical progression approach of the Viola Jones algorithm, measuring both classification precision and recall, as well as algorithmic execution time and battery consumption on Android smartphone devices. Results show that if we manage to approximate this perfect scale selection, we obtain very significant energy savings, motivating a strong research investment on this topic.

References

  1. Barbera, M. V., Kosta, S., Mei, A., and Stefa, J. (2013). To offload or not to offload? the bandwidth and energy costs of mobile cloud computing. In INFOCOM, 2013 Proceedings IEEE, pages 1285-1293. IEEE.
  2. Bhatt, H. S., Bharadwaj, S., Singh, R., and Vatsa, M. (2013). Recognizing surgically altered face images using multiobjective evolutionary algorithm. IEEE Transactions on Information Forensics and Security, 8(1):89-100.
  3. Chen, D., Ren, S., Wei, Y., Cao, X., and Sun, J. (2014). Joint cascade face detection and alignment. In European Conference on Computer Vision, pages 109-122. Springer.
  4. Developers, A. (2014). Android debug bridge. https://developer.android.com/studio/commandline/adb.html.
  5. Fu, Y., Guo, G., and Huang, T. S. (2010). Age synthesis and estimation via faces: A survey. IEEE transactions on pattern analysis and machine intelligence, 32(11):1955-1976.
  6. Hollander, M., Wolfe, D. A., and Chicken, E. (2013). Nonparametric statistical methods. John Wiley & Sons.
  7. Hyett, M. P., Parker, G. B., and Dhall, A. (2016). The utility of facial analysis algorithms in detecting melancholia. In Advances in Face Detection and Facial Image Analysis, pages 359-375. Springer.
  8. Kalal, Z., Mikolajczyk, K., and Matas, J. (2010). Face-tld: Tracking-learning-detection applied to faces. In 2010 IEEE International Conference on Image Processing, pages 3789-3792. IEEE.
  9. Khan, M. A. (2015). A survey of computation offloading strategies for performance improvement of applications running on mobile devices. Journal of Network and Computer Applications, 56:28-40.
  10. Kwon, Y.-W. and Tilevich, E. (2013). Reducing the energy consumption of mobile applications behind the scenes. In ICSM, pages 170-179. Citeseer.
  11. Li, J., Peng, Z., Xiao, B., and Hua, Y. (2015). Make smartphones last a day: Pre-processing based computer vision application offloading. In Sensing, Communication, and Networking (SECON), 2015 12th Annual IEEE International Conference on, pages 462-470. IEEE.
  12. Li, J., Wang, T., and Zhang, Y. (2011). Face detection using surf cascade. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pages 2183-2190. IEEE.
  13. Li, S. Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., and Shum, H. (2002). Statistical learning of multi-view face detection. In European Conference on Computer Vision, pages 67-81. Springer.
  14. Meynet, J., Popovici, V., and Thiran, J.-P. (2007). Face detection with boosted gaussian features. Pattern Recognition, 40(8):2283-2291.
  15. Oneto, L., Ghio, A., Ridella, S., and Anguita, D. (2015). Learning resource-aware classifiers for mobile devices: from regularization to energy efficiency. Neurocomputing, 169:225-235.
  16. Saneiro, M., Santos, O. C., Salmeron-Majadas, S., and Boticario, J. G. (2014). Towards emotion detection in educational scenarios from facial expressions and body movements through multimodal approaches. The Scientific World Journal, 2014.
  17. Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1701-1708.
  18. Viola, M., Jones, M. J., and Viola, P. (2003). Fast multiview face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Citeseer.
  19. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages I-511-I-518. IEEE.
  20. Yang, S., Luo, P., Loy, C. C., and Tang, X. (2016). Wider face: A face detection benchmark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  21. Zhang, L., Chu, R., Xiang, S., Liao, S., and Li, S. Z. (2007). Face detection based on multi-block LBP representation. In International Conference on Biometrics, pages 11-18. Springer.
Download


Paper Citation


in Harvard Style

Perez N., Faria S. and Coimbra M. (2017). Understanding the Energy Saving Potential of Smart Scale Selection in the Viola and Jones Facial Detection Algorithm . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017) ISBN 978-989-758-215-8, pages 122-127. DOI: 10.5220/0006247501220127


in Bibtex Style

@conference{bioimaging17,
author={Noel Perez and Sérgio Faria and Miguel Coimbra},
title={Understanding the Energy Saving Potential of Smart Scale Selection in the Viola and Jones Facial Detection Algorithm},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)},
year={2017},
pages={122-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006247501220127},
isbn={978-989-758-215-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)
TI - Understanding the Energy Saving Potential of Smart Scale Selection in the Viola and Jones Facial Detection Algorithm
SN - 978-989-758-215-8
AU - Perez N.
AU - Faria S.
AU - Coimbra M.
PY - 2017
SP - 122
EP - 127
DO - 10.5220/0006247501220127