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Proceedings Paper

The impact of privacy protection filters on gender recognition
Author(s): Natacha Ruchaud; Grigory Antipov; Pavel Korshunov; Jean-Luc Dugelay; Touradj Ebrahimi; Sid-Ahmed Berrani
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Paper Abstract

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or even outperform human visual system in its ability to detect and recognize objects and their features. In this paper, we focus on the specific task of gender recognition in images when they have been processed by privacy protection filters (e.g., blurring, masking, and pixelization) applied at different strengths. Assuming a privacy protection scenario, we compare the performance of state of the art deep learning algorithms with a subjective evaluation obtained via crowdsourcing to understand how privacy protection filters affect both machine and human vision.

Paper Details

Date Published: 22 September 2015
PDF: 12 pages
Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 959906 (22 September 2015); doi: 10.1117/12.2193647
Show Author Affiliations
Natacha Ruchaud, EURECOM (France)
Grigory Antipov, EURECOM (France)
Orange Labs (France)
Pavel Korshunov, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Jean-Luc Dugelay, EURECOM (France)
Touradj Ebrahimi, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Sid-Ahmed Berrani, Orange Labs (France)


Published in SPIE Proceedings Vol. 9599:
Applications of Digital Image Processing XXXVIII
Andrew G. Tescher, Editor(s)

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