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.
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Notes
- 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.
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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
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DOI: https://doi.org/10.1007/978-3-319-47106-8_18
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