著者

本多 克宏 大森 正博 生方 誠希 野津 亮

出版者

一般社団法人 システム制御情報学会

雑誌

システム制御情報学会論文誌 (ISSN:13425668)

巻号頁・発行日

vol.29, no.3, pp.130-135, 2016-03-15 (Released:2016-06-15)

参考文献数

14

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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.

著者

大森正博

出版者

国立国会図書館

雑誌

レファレンス (ISSN:1349208X)

巻号頁・発行日

no.725, 2011-06