22 September 2015 The impact of privacy protection filters on gender recognition
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Natacha Ruchaud, Natacha Ruchaud, Grigory Antipov, Grigory Antipov, Pavel Korshunov, Pavel Korshunov, Jean-Luc Dugelay, Jean-Luc Dugelay, Touradj Ebrahimi, Touradj Ebrahimi, Sid-Ahmed Berrani, Sid-Ahmed Berrani, } "The impact of privacy protection filters on gender recognition", Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 959906 (22 September 2015); doi: 10.1117/12.2193647; https://doi.org/10.1117/12.2193647
PROCEEDINGS
12 PAGES


SHARE
RELATED CONTENT


Back to Top