Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
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Privacy-preserving Crowd Movement Analysis with Fuzzy k-member Clustering-based Anonymization of Face Images
Katsuhiro HondaMasahiro OmoriSeiki UbukataAkira Notsu
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JOURNALS FREE ACCESS

2016 Volume 29 Issue 3 Pages 130-135

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Abstract

Privacy preservation is an important issue in such personal information analysis as crowd movement analysis with face image recognition. This paper proposes a novel framework for estimating crowd movement characteristics without exactly distinguishing each person, in which personal authentication is performed in eigen-face spaces after fuzzy k-member clustering-based k-anonymization of feature vectors. An experimental result demonstrates that, supported by fuzzy partitioning, the novel framework can improve not only the noise sensitivity and anonymization quality of the conventional k-member clustering but also the reproducibility of crowd movement.

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© 2016 The Institute of Systems, Control and Information Engineers
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