Privacy-protected facial biometric verification via fuzzy forest learning

Richard Jiang, Ahmed Bouridane, Danny Crookes, M. Emre Celebi, Hua-Liang Wei

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
15 Downloads (Pure)


Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacy- related applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.
Original languageEnglish
Pages (from-to)779-790
JournalIEEE Transactions on Fuzzy Systems
Issue number4
Early online date5 Oct 2015
Publication statusPublished - 1 Aug 2016


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