Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Bruce:2016:SSBSE,

• author = "Bobby R. Bruce and Jonathan M. Aitken and Justyna Petke",
• title = "Deep Parameter Optimisation for Face Detection Using the {Viola-Jones} Algorithm in {OpenCV}",
• booktitle = "Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016",
• year = "2016",
• editor = "Federica Sarro and Kalyanmoy Deb",
• volume = "9962",
• series = "LNCS",
• pages = "238--243",
• address = "Raleigh, North Carolina, USA",
• month = "8-10 " # oct,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, OpenCV, NSGA-II, Deep parameter optimisation, Automated parameter tuning, Multi-objective optimisation, GI",
• isbn13 = "978-3-319-47106-8",
• URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2016_SSBSE.pdf",
• DOI = "doi:10.1007/978-3-319-47106-8_18",
• code_url = "https://github.com/BobbyRBruce/DPT-OpenCV",
• size = "6 pages",
• 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 percent if a 1.80 percent classification inaccuracy is permitted (compared to 1.04 percent classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user.",
• notes = "See correction \cite{bruce:RN1707} Tarball of repository is available http://www.cs.ucl.ac.uk/staff/R.Bruce/dpt_opencv.tar.gz",

}

Genetic Programming entries for Bobby R Bruce Jonathan M Aitken Justyna Petke

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