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

Aitken, J.M.

Bruce, B.R.

Petke, J.

Bruce, BR

Aitken, JM

Petke, J

UCL Discovery

Deep Parameter Optimisation for Face DetectionUsing the Viola-Jones Algorithm in OpenCVBobby R. Bruce1, Jonathan M. Aitken2, and Justyna Petke11 CREST Centre, SSE Group, Department of Computer Science, UCL, UK,r.bruce@cs.ucl.ac.uk, j.petke@ucl.ac.uk,2 Department of Automatic Control & Systems Engineering,University of Sheffield, UK,jonathan.aitken@sheffield.ac.ukAbstract. OpenCV is a commonly used computer vision library con-taining a wide variety of algorithms for the AI community. This paperuses deep parameter optimisation to investigate improvements to facedetection using the Viola-Jones algorithm in OpenCV, allowing a trade-off between execution time and classification accuracy. Our results showthat execution time can be decreased by 48% if a 1.80% classificationinaccuracy is permitted (compared to 1.04% classification inaccuracy ofthe original, unmodified algorithm). Further execution time savings arepossible depending on the degree of inaccuracy deemed acceptable bythe user.Keywords: Deep Parameter Optimisation, Automated Parameter Tun-ing, Multi-objective Optimisation, Genetic Improvement, GI, SBSE, OpenCV,Viola-Jones Algorithm1 IntroductionTraditional small mobile robotics applications have limited power and computingcapacity. This is further complicated for Unmanned Aerial Vehicles (UAVs),which have limited battery-life and thus any excess weight is detrimental to thetime that can be spent in the air. The efficiency and accuracy of the processing isthus essential in an Unmanned Aerial Vehicle (UAV) performing tasks. Typicallythese tasks can use visual servoing in order to direct flights to locate objects ofinterest [5], or to provide a larger field of view for ground-based vehicles [7]. Thisis especially important in areas in which the Global Positioning System (GPS)is unavailable, where an aerial vehicle can be used to localise a ground vehicleand provide extra information about routing [2].For example, if the UAV is performing a visual survey of a region, any repeti-tion of a route is wasteful. The optimisation of any visual processing is essentialso that areas do not need to be re-covered and thus battery capacity is notwasted. Speeding up visual processing leads to images being processed at fasterrates which allows the capture of more data [11].Therefore, we propose to optimise OpenCV, a very popular computer visionlibrary within the robotics community, using the recently introduced techniqueof deep parameter optimisation [14]. Our results show that we can achieve signifi-cant efficiency gains when we trade-off runtime and image classification accuracy.The following sections present the details of our approach.2 OpenCVOpenCV1 is a library for computer vision [3]. It was developed by Intel, thenWillow Garage, leading to its integration in the popular robotic developmentarchitecture – Robot Operating System (ROS) [8] and wide uptake within therobotics as well as the computer vision community. It is now maintained byItseez, a software company that specialises in optimisation of real-world appli-cations in computer vision, pattern recognition and machine learning2.Face detection in OpenCV is commonly implemented using the Viola-Jonesalgorithm [12, 13]. The Viola-Jones algorithm searches an image, at multiplescales, shifting through the image one pixel at a time, for a collection of haarfeatures, which are shapes of binary values defining areas of light and darkness,components of the object separate from the background. The selected set of haarfeatures defines the detected objects. There is a common set of haar featuresimplemented in OpenCV that detects human faces, and a cascade classifier canbe trained with an appropriate set.3 Deep Parameter OptimisationDeep parameter optimisation [14] is a technique that delves deeper into pa-rameters that can affect non-functional program properties than traditional ap-proaches (e.g., used in the machine learning community [6]). This forms a largersearch-space opening new routes over which optimisation can be performed.There is a three-step process for performing deep parameter optimisation: (1)Discovery of the locations for deep parameters; (2) Exposing deep parametersto be available for tuning; (3) Search-based tuning of the exposed parameters.4 Related WorkPreviously studies have investigated the potential of optimising the Viola-Jonesalgorithm or adjusting it to perform more favourably under differing condi-tions [1, 9, 10].Aby et al. [1] explored optimisation of the Viola-Jones algorithm on an em-bedded, single-board, computing platform – the Beagle Board. Rather than di-rectly optimising the algorithm, they scheduled the heavy computational tasks1 OpenCV’s source code is available at: https://github.com/Itseez/opencv/2 Itseez software company website: http://itseez.com/on the Digital Signal Processor, freeing up the main ARM processor to completeancillary computational tasks. Whilst this technique provides an improvementof processing time of the Viola-Jones algorithm it does not attempt to optimisethe algorithm itself.Rahmen et al. [9] developed an algorithm that uses skin colour and shapeprocessing to detect faces. Initially this segments the images using typical skincolours before looking for smaller shapes that characterise faces. They achievedgood performance, and indicate a favourable improvements in processing speed.Ren et al. [10] applied a series of optimisation techniques in order to improveperformance. They focused on removing the need to use dedicated extra process-ing power, rather than looking for software-based solutions. They tested threedifferent optimisation approaches:– Data Reduction – reducing the resolution of images used for face identi-fication, increasing the shift between images from the standard one pixel,increasing the sizes used at each scale step and defining a larger minimumface size terminating the algorithm more quickly.– Search Reduction – using key frames to limit the number of frames that needprocessing for a given video sequence.– Numerical Reduction – using fixed-point formatted numbers rather thanfloating point to save on computation.This paper provides an extension of this optimisation work as it applies deepparameter optimisation to the Viola-Jones algorithm itself. Rather than shiftingprocessing, or attempting pre-filtering, we adjust the parameters themselves. Un-like the work of [10] the adjustments made throughout the optimisation are notlimited to different areas, but operate across the complete algorithm. By usingdeep parameter optimisation, we can expose hidden options for optimisation.5 Experimental SetupGiven OpenCV is a library, we developed a small command-line level programto utilise the OpenCV’s functionality we wished to be optimised. This program,classify_images, took a directory of images as a lone argument. When ex-ecuted classify_images produces output identifying which images containedfaces and which did not. classify_images utilises the CascadeClassifier::detectMultiScale method with CascadeClassifier initialised usinghaarcascade_frontalface_alt.xml (included by default in OpenCV).We created a dataset of 10,000 images which contain faces3 and 10,000 imageswhich do not4. This was then split into a training set containing 1,500 imageswith faces and 1,500 without, and a test set containing 8,500 images with faces3 Obtained from the University of Massachusetts ‘Labelled Faces In The wild’ dataset- http://vis-www.cs.umass.edu/lfw/lfw.tgz4 Obtained from the Caltech-256 dataset – http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tarand 8,500 without. Prior to any form of optimisation classify_images incor-rectly classified 0.90% of the training set and 1.04% of the test set.We then profiled the software to find which files were the most heavilyutilised in OpenCV when classifying images. We found that the top two fileswere cascadedetect.cpp and cascadedetect.hpp. We then proceeded to ex-tract all integer constants from these files. This process involved using a regularexpression to highlight all instances of integer constants. Before doing so we car-ried out a replacement of all occurrences of [variable]++ to [variable]+=1,increasing the number of constants available for extraction.We then replaced all instances of integer constants found with unique CDefine Compilation Macros. These were extracted to a file called defines.hppwhich was then included in both cascadedetect.cpp and cascadedetect.hpp.In total, defines.hpp contained 537 integer constants. defines.hpp can be seenas a source-code level configuration file which we altered.While it would have been possible to proceed at this point with the parametertuning process, considerable savings can be made by carrying out sensitivityanalysis – the process of selecting a subset of parameters to optimise the desirednon-functional properties.For each of the 537 integer constants we first added one, compiled the OpenCVlibrary, then run classify_images on a single face image randomly selectedfrom the training set. If classify_images compiled, run, and produced a resultwithout crashing, it passed what we refer to as ‘stage 1’. If an integer passed‘stage 1’ we then added 50 to the integer value. classify_images was thencompiled with the modified OpenCV and run on the training set. To pass thisstage (‘stage 2’) the modified version had to complete compilation and completeexecution in a time different to the original (outside of the 95% confidence inter-val for the original, unmodified, classify_images run 100 times on the trainingset). ‘stage 1’ can be viewed as a step to filter out parameters that are too sen-sitive, while ‘stage 2’ can be viewed as a step to filter out those that are notsensitive enough. After these two stages of sensitivity analysis we were left with51 deep parameters for optimisation.We tuned these parameters using the NSGA-II algorithm [4] implementedin the MOEA framework5. For the execution time objective we used UNIX’stime utility on classify_images when classifying the training set. The secondobjective, classification inaccuracy, was calculated as a percentage of incorrectclassifications by classify_images on the training set. NSGA-II attempts tominimize both of these objectives. We further reduced the search-space by onlyallowing parameters to be increased to a maximum of 64 and to be decreased toa minimum of 0.We ran NSGA-II on 100 individuals over 10 generations in an Ubuntu 14.04.4m4.large Amazon EC2 Instance (2x2.4GHz Intel Xeon E5-2676 v3 processor,8GiB of memory, SSD Storage). The initial generation was seeded with an indi-vidual containing the original parameter settings. The remainder of the initial5 MOEA framework available at: http://moeaframework.org/population was generated by iterating through the parameters and generating avariant equal to the original but with the parameter being increased by 1 or 2.Once complete, the MOEA framework returned the Pareto front of solutions.To ensure these results were not over-fitted to the training set, we ran each Paretooptimal solution on the test set, removing any which crash or were dominatedby other solutions to produce the final Pareto optimal set.6 ResultsThe NSGA-II algorithm produced a Pareto front that contained 14 solutionswhen run on the training set. When ran each of these Pareto optimal solutionson the test set, one failed to complete execution and another was dominatedby other solutions in the set thus leaving 12 Pareto optimal solutions and theoriginal, unaltered program which was also found to be Pareto optimal whenrun on the test set6. These are shown in Figure 1 (the original program includedas the left-most solution).0 10 20 30 40 50050100150200Incorrect Classification (%)Time (s)Fig. 1. The Pareto front of solutions when run on the test set of 17,000 images.7 ConclusionsWe used deep parameter optimisation to investigate improvements to face de-tection using the Viola-Jones algorithm in OpenCV, allowing for a trade-offbetween execution time and classification accuracy. In this study, a basic formof deep parameter optimisation decreased the execution time of the Viola-Jonesalgorithm by 48% with a 1.80% classification inaccuracy when evaluated on a6 The source for the deep parameter optimisation algorithm we used and data dis-cussed here is available from: https://github.com/BobbyBruce1990/DPT-OpenCV.gittest set of 17,000 images (compared to a 1.04% inaccuracy when using the orig-inal algorithm). This technique shows the capacity for improvement within awidely used implementation of the Viola-Jones algorithm and provides a soundbasis for further exploitation of more complex Search Based Software Engineer-ing (SBSE) methods. The source to achieve this has been made openly availableon GitHub6.References1. Aby, P., Jose, A., Dinu, L., John, J., Sabarinath, G.: Implementation and optimiza-tion of embedded face detection system. In: International Conference on SignalProcessing, Communication, Computing and Networking Technologies (ICSCCN).pp. 250–253 (2011)2. Aitken, J.M., McAree, O., Veres, S.: Symbiotic relationship between robots – a ROSARDrone/YouBot library. In: Proceedings of UKACC International Conference onControl (CONTROL) (2016)3. Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCVlibrary. ” O’Reilly Media, Inc.” (2008)4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjectivegenetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on6(2), 182–197 (2002)5. 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. Journal of Field Robotics 25(1-2), 89–110 (2008)6. Hoos, H.H.: Autonomous Search, chap. Automated Algorithm Configuration andParameter Tuning, pp. 37–71. Springer Berlin Heidelberg, Berlin, Heidelberg(2012), http://dx.doi.org/10.1007/978-3-642-21434-9_37. 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 forsituational awareness. Journal of Field Robotics 24(11-12), 991–1014 (2007)8. 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 onopen source software. vol. 3, p. 5 (2009)9. Rahman, M., Ren, J., Kehtarnavaz, N.: Real-time implementation of robust facedetection on mobile platforms. In: IEEE International Conference on Acoustics,Speech and Signal Processing. pp. 1353–1356 (2009)10. Ren, J., Kehtarnavaz, N., Estevez, L.: Real-time optimization of Viola-Jones facedetection for mobile platforms. In: IEEE Circuits and Systems Workshop: System-on-Chip-Design, Applications, Integration, and Software. pp. 1–4 (2008)11. Shubina, K., Tsotsos, J.K.: Visual search for an object in a 3D environment using amobile robot. Computer Vision and Image Understanding 114(5), 535–547 (2010)12. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simplefeatures. In: Proceedings of the IEEE Computer Society Conference on ComputerVision and Pattern Recognition. IEEE (2001)13. Viola, P., Jones, M.: Robust real-time face detection. International journal of com-puter vision 57(2), 137–154 (2004)14. 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)

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

White Rose Research Online

This is a repository copy of Deep parameter optimisation for face detection using the Viola-Jones algorithm in OpenCV.White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/146927/Version: Accepted VersionProceedings Paper:Bruce, B.R., Aitken, J.M. orcid.org/0000-0003-4204-4020 and Petke, J. (2016) Deep parameter optimisation for face detection using the Viola-Jones algorithm in OpenCV. In: Sarro, F and Deb, K, (eds.) Search Based Software Engineering. SSBSE 2016. SSBSE 2016: International Symposium on Search Based Software Engineering, 08-10 Oct 2016, Raleigh, NC, USA. Lecture Notes in Computer Science (9962). Springer International Publishing , pp. 238-243. ISBN 9783319471051 https://doi.org/10.1007/978-3-319-47106-8_18This is a post-peer-review, pre-copyedit version of an article published in SSBSE 2016: Search Based Software Engineering. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-47106-8_18eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Deep Parameter Optimisation for Face DetectionUsing the Viola-Jones Algorithm in OpenCVBobby R. Bruce1, Jonathan M. Aitken2, and Justyna Petke11 CREST Centre, SSE Group, Department of Computer Science, UCL, UK,r.bruce@cs.ucl.ac.uk, j.petke@ucl.ac.uk,2 Department of Automatic Control & Systems Engineering,University of Sheffield, UK,jonathan.aitken@sheffield.ac.ukAbstract. OpenCV is a commonly used computer vision library con-taining a wide variety of algorithms for the AI community. This paperuses deep parameter optimisation to investigate improvements to facedetection using the Viola-Jones algorithm in OpenCV, allowing a trade-off between execution time and classification accuracy. Our results showthat execution time can be decreased by 48% if a 1.80% classificationinaccuracy is permitted (compared to 1.04% classification inaccuracy ofthe original, unmodified algorithm). Further execution time savings arepossible depending on the degree of inaccuracy deemed acceptable bythe user.Keywords: Deep Parameter Optimisation, Automated Parameter Tun-ing, Multi-objective Optimisation, Genetic Improvement, GI, SBSE, OpenCV,Viola-Jones Algorithm1 IntroductionTraditional small mobile robotics applications have limited power and computingcapacity. This is further complicated for Unmanned Aerial Vehicles (UAVs),which have limited battery-life and thus any excess weight is detrimental to thetime that can be spent in the air. The efficiency and accuracy of the processing isthus essential in an Unmanned Aerial Vehicle (UAV) performing tasks. Typicallythese tasks can use visual servoing in order to direct flights to locate objects ofinterest [5], or to provide a larger field of view for ground-based vehicles [7]. Thisis especially important in areas in which the Global Positioning System (GPS)is unavailable, where an aerial vehicle can be used to localise a ground vehicleand provide extra information about routing [2].For example, if the UAV is performing a visual survey of a region, any repeti-tion of a route is wasteful. The optimisation of any visual processing is essentialso that areas do not need to be re-covered and thus battery capacity is notwasted. Speeding up visual processing leads to images being processed at fasterrates which allows the capture of more data [11].Therefore, we propose to optimise OpenCV, a very popular computer visionlibrary within the robotics community, using the recently introduced techniqueof deep parameter optimisation [14]. Our results show that we can achieve signifi-cant efficiency gains when we trade-off runtime and image classification accuracy.The following sections present the details of our approach.2 OpenCVOpenCV1 is a library for computer vision [3]. It was developed by Intel, thenWillow Garage, leading to its integration in the popular robotic developmentarchitecture – Robot Operating System (ROS) [8] and wide uptake within therobotics as well as the computer vision community. It is now maintained byItseez, a software company that specialises in optimisation of real-world appli-cations in computer vision, pattern recognition and machine learning2.Face detection in OpenCV is commonly implemented using the Viola-Jonesalgorithm [12, 13]. The Viola-Jones algorithm searches an image, at multiplescales, shifting through the image one pixel at a time, for a collection of haarfeatures, which are shapes of binary values defining areas of light and darkness,components of the object separate from the background. The selected set of haarfeatures defines the detected objects. There is a common set of haar featuresimplemented in OpenCV that detects human faces, and a cascade classifier canbe trained with an appropriate set.3 Deep Parameter OptimisationDeep parameter optimisation [14] is a technique that delves deeper into pa-rameters that can affect non-functional program properties than traditional ap-proaches (e.g., used in the machine learning community [6]). This forms a largersearch-space opening new routes over which optimisation can be performed.There is a three-step process for performing deep parameter optimisation: (1)Discovery of the locations for deep parameters; (2) Exposing deep parametersto be available for tuning; (3) Search-based tuning of the exposed parameters.4 Related WorkPreviously studies have investigated the potential of optimising the Viola-Jonesalgorithm or adjusting it to perform more favourably under differing condi-tions [1, 9, 10].Aby et al. [1] explored optimisation of the Viola-Jones algorithm on an em-bedded, single-board, computing platform – the Beagle Board. Rather than di-rectly optimising the algorithm, they scheduled the heavy computational tasks1 OpenCV’s source code is available at: https://github.com/Itseez/opencv/2 Itseez software company website: http://itseez.com/on the Digital Signal Processor, freeing up the main ARM processor to completeancillary computational tasks. Whilst this technique provides an improvementof processing time of the Viola-Jones algorithm it does not attempt to optimisethe algorithm itself.Rahmen et al. [9] developed an algorithm that uses skin colour and shapeprocessing to detect faces. Initially this segments the images using typical skincolours before looking for smaller shapes that characterise faces. They achievedgood performance, and indicate a favourable improvements in processing speed.Ren et al. [10] applied a series of optimisation techniques in order to improveperformance. They focused on removing the need to use dedicated extra process-ing power, rather than looking for software-based solutions. They tested threedifferent optimisation approaches:– Data Reduction – reducing the resolution of images used for face identi-fication, increasing the shift between images from the standard one pixel,increasing the sizes used at each scale step and defining a larger minimumface size terminating the algorithm more quickly.– Search Reduction – using key frames to limit the number of frames that needprocessing for a given video sequence.– Numerical Reduction – using fixed-point formatted numbers rather thanfloating point to save on computation.This paper provides an extension of this optimisation work as it applies deepparameter optimisation to the Viola-Jones algorithm itself. Rather than shiftingprocessing, or attempting pre-filtering, we adjust the parameters themselves. Un-like the work of [10] the adjustments made throughout the optimisation are notlimited to different areas, but operate across the complete algorithm. By usingdeep parameter optimisation, we can expose hidden options for optimisation.5 Experimental SetupGiven OpenCV is a library, we developed a small command-line level programto utilise the OpenCV’s functionality we wished to be optimised. This program,classify_images, took a directory of images as a lone argument. When ex-ecuted classify_images produces output identifying which images containedfaces and which did not. classify_images utilises the CascadeClassifier::detectMultiScale method with CascadeClassifier initialised usinghaarcascade_frontalface_alt.xml (included by default in OpenCV).We created a dataset of 10,000 images which contain faces3 and 10,000 imageswhich do not4. This was then split into a training set containing 1,500 imageswith faces and 1,500 without, and a test set containing 8,500 images with faces3 Obtained from the University of Massachusetts ‘Labelled Faces In The wild’ dataset- http://vis-www.cs.umass.edu/lfw/lfw.tgz4 Obtained from the Caltech-256 dataset – http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tarand 8,500 without. Prior to any form of optimisation classify_images incor-rectly classified 0.90% of the training set and 1.04% of the test set.We then profiled the software to find which files were the most heavilyutilised in OpenCV when classifying images. We found that the top two fileswere cascadedetect.cpp and cascadedetect.hpp. We then proceeded to ex-tract all integer constants from these files. This process involved using a regularexpression to highlight all instances of integer constants. Before doing so we car-ried out a replacement of all occurrences of [variable]++ to [variable]+=1,increasing the number of constants available for extraction.We then replaced all instances of integer constants found with unique CDefine Compilation Macros. These were extracted to a file called defines.hppwhich was then included in both cascadedetect.cpp and cascadedetect.hpp.In total, defines.hpp contained 537 integer constants. defines.hpp can be seenas a source-code level configuration file which we altered.While it would have been possible to proceed at this point with the parametertuning process, considerable savings can be made by carrying out sensitivityanalysis – the process of selecting a subset of parameters to optimise the desirednon-functional properties.For each of the 537 integer constants we first added one, compiled the OpenCVlibrary, then run classify_images on a single face image randomly selectedfrom the training set. If classify_images compiled, run, and produced a resultwithout crashing, it passed what we refer to as ‘stage 1’. If an integer passed‘stage 1’ we then added 50 to the integer value. classify_images was thencompiled with the modified OpenCV and run on the training set. To pass thisstage (‘stage 2’) the modified version had to complete compilation and completeexecution in a time different to the original (outside of the 95% confidence inter-val for the original, unmodified, classify_images run 100 times on the trainingset). ‘stage 1’ can be viewed as a step to filter out parameters that are too sen-sitive, while ‘stage 2’ can be viewed as a step to filter out those that are notsensitive enough. After these two stages of sensitivity analysis we were left with51 deep parameters for optimisation.We tuned these parameters using the NSGA-II algorithm [4] implementedin the MOEA framework5. For the execution time objective we used UNIX’stime utility on classify_images when classifying the training set. The secondobjective, classification inaccuracy, was calculated as a percentage of incorrectclassifications by classify_images on the training set. NSGA-II attempts tominimize both of these objectives. We further reduced the search-space by onlyallowing parameters to be increased to a maximum of 64 and to be decreased toa minimum of 0.We ran NSGA-II on 100 individuals over 10 generations in an Ubuntu 14.04.4m4.large Amazon EC2 Instance (2x2.4GHz Intel Xeon E5-2676 v3 processor,8GiB of memory, SSD Storage). The initial generation was seeded with an indi-vidual containing the original parameter settings. The remainder of the initial5 MOEA framework available at: http://moeaframework.org/population was generated by iterating through the parameters and generating avariant equal to the original but with the parameter being increased by 1 or 2.Once complete, the MOEA framework returned the Pareto front of solutions.To ensure these results were not over-fitted to the training set, we ran each Paretooptimal solution on the test set, removing any which crash or were dominatedby other solutions to produce the final Pareto optimal set.6 ResultsThe NSGA-II algorithm produced a Pareto front that contained 14 solutionswhen run on the training set. When ran each of these Pareto optimal solutionson the test set, one failed to complete execution and another was dominatedby other solutions in the set thus leaving 12 Pareto optimal solutions and theoriginal, unaltered program which was also found to be Pareto optimal whenrun on the test set6. These are shown in Figure 1 (the original program includedas the left-most solution).0 10 20 30 40 50050100150200Incorrect Classification (%)Time (s)Fig. 1. The Pareto front of solutions when run on the test set of 17,000 images.7 ConclusionsWe used deep parameter optimisation to investigate improvements to face de-tection using the Viola-Jones algorithm in OpenCV, allowing for a trade-offbetween execution time and classification accuracy. In this study, a basic formof deep parameter optimisation decreased the execution time of the Viola-Jonesalgorithm by 48% with a 1.80% classification inaccuracy when evaluated on a6 The source for the deep parameter optimisation algorithm we used and data dis-cussed here is available from: https://github.com/BobbyBruce1990/DPT-OpenCV.gittest set of 17,000 images (compared to a 1.04% inaccuracy when using the orig-inal algorithm). This technique shows the capacity for improvement within awidely used implementation of the Viola-Jones algorithm and provides a soundbasis for further exploitation of more complex Search Based Software Engineer-ing (SBSE) methods. The source to achieve this has been made openly availableon GitHub6.References1. Aby, P., Jose, A., Dinu, L., John, J., Sabarinath, G.: Implementation and optimiza-tion of embedded face detection system. In: International Conference on SignalProcessing, Communication, Computing and Networking Technologies (ICSCCN).pp. 250–253 (2011)2. Aitken, J.M., McAree, O., Veres, S.: Symbiotic relationship between robots – a ROSARDrone/YouBot library. In: Proceedings of UKACC International Conference onControl (CONTROL) (2016)3. Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCVlibrary. ” O’Reilly Media, Inc.” (2008)4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjectivegenetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on6(2), 182–197 (2002)5. 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. Journal of Field Robotics 25(1-2), 89–110 (2008)6. Hoos, H.H.: Autonomous Search, chap. Automated Algorithm Configuration andParameter Tuning, pp. 37–71. Springer Berlin Heidelberg, Berlin, Heidelberg(2012), http://dx.doi.org/10.1007/978-3-642-21434-9_37. 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 forsituational awareness. Journal of Field Robotics 24(11-12), 991–1014 (2007)8. 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 onopen source software. vol. 3, p. 5 (2009)9. Rahman, M., Ren, J., Kehtarnavaz, N.: Real-time implementation of robust facedetection on mobile platforms. In: IEEE International Conference on Acoustics,Speech and Signal Processing. pp. 1353–1356 (2009)10. Ren, J., Kehtarnavaz, N., Estevez, L.: Real-time optimization of Viola-Jones facedetection for mobile platforms. In: IEEE Circuits and Systems Workshop: System-on-Chip-Design, Applications, Integration, and Software. pp. 1–4 (2008)11. Shubina, K., Tsotsos, J.K.: Visual search for an object in a 3D environment using amobile robot. Computer Vision and Image Understanding 114(5), 535–547 (2010)12. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simplefeatures. In: Proceedings of the IEEE Computer Society Conference on ComputerVision and Pattern Recognition. IEEE (2001)13. Viola, P., Jones, M.: Robust real-time face detection. International journal of com-puter vision 57(2), 137–154 (2004)14. 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)

Deep parameter optimisation for face detection using the Viola-Jones algorithm in OpenCV

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Deep parameter optimisation for face detection using the Viola-Jones algorithm in OpenCV

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