2016 Volume 29 Issue 3 Pages 130-135
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.