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Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning

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Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning. / Jiang, Richard; Bouridane, Ahmed; Crookes, Danny; Celebi, M. Emre; Wei, Hua-Liang.

In: IEEE Transactions on Fuzzy Systems, Vol. 24, No. 4, 01.08.2016, p. 779-790.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Jiang, R, Bouridane, A, Crookes, D, Celebi, ME & Wei, H-L 2016, 'Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning', IEEE Transactions on Fuzzy Systems, vol. 24, no. 4, pp. 779-790. https://doi.org/10.1109/TFUZZ.2015.2486803

APA

Jiang, R., Bouridane, A., Crookes, D., Celebi, M. E., & Wei, H-L. (2016). Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning. IEEE Transactions on Fuzzy Systems, 24(4), 779-790. https://doi.org/10.1109/TFUZZ.2015.2486803

Vancouver

Jiang R, Bouridane A, Crookes D, Celebi ME, Wei H-L. Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning. IEEE Transactions on Fuzzy Systems. 2016 Aug 1;24(4):779-790. https://doi.org/10.1109/TFUZZ.2015.2486803

Author

Jiang, Richard ; Bouridane, Ahmed ; Crookes, Danny ; Celebi, M. Emre ; Wei, Hua-Liang. / Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning. In: IEEE Transactions on Fuzzy Systems. 2016 ; Vol. 24, No. 4. pp. 779-790.

Bibtex

@article{b79f13c3219d46ffbce8322b351d988a,
title = "Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning",
abstract = "Although visual surveillance has emerged as an effective technology 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.",
keywords = "Chaotic pattern, ensemble learning, face scrambling, facial biometrics, fuzzy random forest, privacy",
author = "Richard Jiang and Ahmed Bouridane and Danny Crookes and Celebi, {M. Emre} and Hua-Liang Wei",
year = "2016",
month = aug,
day = "1",
doi = "10.1109/TFUZZ.2015.2486803",
language = "English",
volume = "24",
pages = "779--790",
journal = "IEEE Transactions on Fuzzy Systems",
issn = "1063-6706",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "4",

}

RIS

TY - JOUR

T1 - Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning

AU - Jiang, Richard

AU - Bouridane, Ahmed

AU - Crookes, Danny

AU - Celebi, M. Emre

AU - Wei, Hua-Liang

PY - 2016/8/1

Y1 - 2016/8/1

N2 - Although visual surveillance has emerged as an effective technology 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.

AB - Although visual surveillance has emerged as an effective technology 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.

KW - Chaotic pattern

KW - ensemble learning

KW - face scrambling

KW - facial biometrics

KW - fuzzy random forest

KW - privacy

U2 - 10.1109/TFUZZ.2015.2486803

DO - 10.1109/TFUZZ.2015.2486803

M3 - Journal article

VL - 24

SP - 779

EP - 790

JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

SN - 1063-6706

IS - 4

ER -