Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV
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- Proceedings paper
- Science & Technology, Technology, Computer Science, Software Engineering, Computer Science, Theory & Methods, Computer Science, Deep parameter optimisation, Automated parameter tuning, Multi-objective optimisation, Genetic improvement, GI, SBSE, OpenCV, Viola-Jones Algorithm, Search