Face Image Anonymization by Fuzzy k-member Clustering and Crowd Movement Analysis

Abstract

k-anonymization by fuzzy k-member clustering has been shown to be useful in privacy-preserving multivariate data analysis with lower information losses. In this research, the anonymization model is applied to eigen-face-based personal identification system and the applicability to crowd movement analysis with fuzzy k-anonymization of face images is discussed. While k-anonymization process makes it difficult to identify each person, the anonymized information is shown to be still useful in capturing crowd movement in large public facilities.