Abstract
Blurring faces on images may be required for anonymity reasons. This may be achieved using face detectors that return boxes potentially containing faces. The most direct way to exploit these detectors is to combine them in order to obtain a more efficient face detection system, producing more accurate boxes. However, contrary to detection, blurring is actually a decision problem situated rather at the pixel level than the box level. Accordingly, we propose in this paper a face blurring system based on face detectors, which operates at the pixel-level. First, for each pixel, detector outputs are converted into a common representation known as belief function using a calibration procedure. Then, calibrated outputs are combined using Dempster’s rule. This pixel-based approach does not have some shortcomings of a state-of-the-art box-based approach, and shows better performances on a classical face dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Due to lack of space, we must refrain from recalling the definition of \(m^\varOmega _S\) obtained under this calibration. We refer the interested reader to [12].
References
Denoeux, T.: Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recogn. 30(7), 1095–1107 (1997)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Faux, F., Luthon, F.: Theory of evidence for face detection and tracking. Int. J. Approximate Reason. 53(5), 728–746 (2012)
Hadid, A., Pietikäinen, M., Ahonen, T.: A discriminative feature space for detecting, recognizing faces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision, Pattern Recognition (CVPR), vol. 2, pp. II-797, 2004 (2011)
Jain, V., Learned-Miller, E.: A benchmark for face detection in unconstrained settings. Technical report UM-CS–009, U Massachusetts (2010)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 130–136 (1997)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10(3), 61–74 (1999)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66, 191–243 (1994)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Xu, P., Davoine, F., Denœux, T.: Evidential combination of pedestrian detectors. In: British Machine Vision Conference (2014)
Xu, P., Davoine, F., Zha, H., Denoeux, T.: Evidential calibration of binary SVM classifiers. Int. J. Approximate Reason. 72, 55–70 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Minary, P., Pichon, F., Mercier, D., Lefèvre, É., Droit, B. (2016). An Evidential Pixel-Based Face Blurring Approach. In: Vejnarová, J., Kratochvíl, V. (eds) Belief Functions: Theory and Applications. BELIEF 2016. Lecture Notes in Computer Science(), vol 9861. Springer, Cham. https://doi.org/10.1007/978-3-319-45559-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-45559-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45558-7
Online ISBN: 978-3-319-45559-4
eBook Packages: Computer ScienceComputer Science (R0)