Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning

Steven Puttemans, Can Ergun, Toon Goedemé

Abstract

Computer vision has almost solved the issue of in the wild face detection, using complex techniques like convolutional neural networks. On the contrary many open source computer vision frameworks like OpenCV have not yet made the switch to these complex techniques and tend to depend on well established algorithms for face detection, like the cascade classification pipeline suggested by Viola and Jones. The accuracy of these basic face detectors on public datasets like FDDB stays rather low, mainly due to the high number of false positive detections. We propose several adaptations to the current existing face detection model training pipeline of OpenCV. We improve the training sample generation and annotation procedure, and apply an active learning strategy. These boost the accuracy of in the wild face detection on the FDDB dataset drastically, closing the gap towards the accuracy gained by CNN-based face detectors. The proposed changes allow us to provide an improved face detection model to OpenCV, achieving a remarkably high precision at an acceptable recall, two critical requirements for further processing pipelines like person identification, etc.

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Paper Citation


in Harvard Style

Puttemans S., Ergun C. and Goedemé T. (2017). Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 396-404. DOI: 10.5220/0006256003960404


in Bibtex Style

@conference{visapp17,
author={Steven Puttemans and Can Ergun and Toon Goedemé},
title={Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={396-404},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006256003960404},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning
SN - 978-989-758-226-4
AU - Puttemans S.
AU - Ergun C.
AU - Goedemé T.
PY - 2017
SP - 396
EP - 404
DO - 10.5220/0006256003960404