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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