Abstract
OpenCV is a commonly used computer vision library containing a wide variety of algorithms for the AI community. This paper uses deep parameter optimisation to investigate improvements to face detection using the Viola-Jones algorithm in OpenCV, allowing a trade-off between execution time and classification accuracy. Our results show that execution time can be decreased by 48 % if a 1.80 % classification inaccuracy is permitted (compared to 1.04 % classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
OpenCV’s source code is available at: https://github.com/Itseez/opencv/.
- 2.
Itseez software company website: http://itseez.com/.
- 3.
Obtained from the University of Massachusetts ‘Labelled Faces In The wild’ dataset - http://vis-www.cs.umass.edu/lfw/lfw.tgz.
- 4.
Obtained from the Caltech-256 dataset – http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar.
- 5.
MOEA framework available at: http://moeaframework.org/.
- 6.
The source for the deep parameter optimisation algorithm we used and data discussed here is available from: https://github.com/BobbyBruce1990/DPT-OpenCV.git.
References
Aby, P., Jose, A., Dinu, L., John, J., Sabarinath, G.: Implementation and optimization of embedded face detection system. In: International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), pp. 250–253 (2011)
Aitken, J.M., McAree, O., Veres, S.: Symbiotic relationship between robots - a ROS ARDrone/YouBot library. In: Proceedings of UKACC International Conference on Control (CONTROL) (2016)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Inc., Upper Saddle River (2008)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Goodrich, M.A., Morse, B.S., Gerhardt, D., Cooper, J.L., Quigley, M., Adams, J.A., Humphrey, C.: Supporting wilderness search and rescue using a camera-equipped mini UAV. J. Field Robot. 25(1–2), 89–110 (2008)
Hoos, H.H.: Automated algorithm configuration and parameter tuning. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 37–71. Springer, Heidelberg (2012). doi:10.1007/978-3-642-21434-9_3
Hsieh, M.A., Cowley, A., Keller, J.F., Chaimowicz, L., Grocholsky, B., Kumar, V., Taylor, C.J., Endo, Y., Arkin, R.C., Jung, B., Wolf, D.F., Sukhatme, G.S., MacKenzie, D.C.: Adaptive teams of autonomous aerial and ground robots for situational awareness. J. Field Robot. 24(11–12), 991–1014 (2007)
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5 (2009)
Rahman, M., Ren, J., Kehtarnavaz, N.: Real-time implementation of robust face detection on mobile platforms. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1353–1356 (2009)
Ren, J., Kehtarnavaz, N., Estevez, L.: Real-time optimization of Viola-Jones face detection for mobile platforms. In: IEEE Circuits and Systems Workshop: System-on-Chip-Design, Applications, Integration, and Software, pp. 1–4 (2008)
Shubina, K., Tsotsos, J.K.: Visual search for an object in a 3D environment using a mobile robot. Comput. Vis. Image Underst. 114(5), 535–547 (2010)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE (2001)
Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Wu, F., Weimer, W., Harman, M., Jia, Y., Krinke, J.: Deep parameter optimisation. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1375–1382 (2015)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Bruce, B.R., Aitken, J.M., Petke, J. (2016). Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-47106-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47105-1
Online ISBN: 978-3-319-47106-8
eBook Packages: Computer ScienceComputer Science (R0)