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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9962)


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.


Deep parameter optimisation Automated parameter tuning Multi-objective optimisation Genetic improvement GI SBSE OpenCV Viola-Jones Algorithm 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.SSE Group, Department of Computer Science, CREST CentreUCLLondonUK
  2. 2.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK

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