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Privacy preserved duplicate check using multi-biometric fusion

: Butt, Moazzam; Damer, Naser; Rathgeb, Christian

Institute of Electrical and Electronics Engineers -IEEE-; International Society of Information Fusion -ISIF-:
FUSION 2014, 17th International Conference on Information Fusion : 7 -10 July 2014, Salamanca, Spain
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-1634-4
ISBN: 9788490123553
7 S.
International Conference on Information Fusion (FUSION) <17, 2014, Salamanca>
Fraunhofer IGD ()
multi-biometrics; data protection; feature extraction; authentication; face recognition; Iris recognition; visual database systems; privacy enhancing technologies; Business Field: Digital society; Research Area: Human Computer Interaction (HCI)

Automated recognition of individuals can be performed using biometrics without any requirement of explicit knowledge of a PIN or a password. On the one side biometrics has given convenience to citizens as they do not need to memorize a bunch of passwords, but on the other side intra (inter) class variations within (between) biometric features makes biometric authentications untrustworthy. Therefore, decisions based on biometric authentications are made more reliable by using several biometric authentications performed on single or multiple biometric modalities (i.e. multi-biometric fusion).
This paper describes a method to identify if a person tries to re-enrol him/herself in a database, when he/she is already enrolled. This is referred to as duplicate check. In this work, duplicate check is performed using two modalities: face and iris. The templates used during the duplicate check are compliant to the ISO/IEC 24745 - Biometric information protection. Such templates are known as protected biometric templates. The protected biometric templates used in this work are generated using the recently published irreversible transformation based on Bloom filters. Scores are calculated from face and iris Bloom filters based templates by comparison with their respective enrolment templates using the normalized Hamming distance. As a decision of the duplicate check, these scores from both modalities are fused with appropriate weighting factors in order to get improved performance compared to using single individual modalities. The presented scheme is experimentally validated using two public benchmark databases namely the LFW and the CASIA-Iris-Thousand databases for face and iris respectively.