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Helper data scheme for 2D cancelable face recognition using bloom filters

: Butt, Moazzam; Damer, Naser

Muštra, M. ; Institute of Electrical and Electronics Engineers -IEEE-, Region Europe, Middle East, Africa; Institute of Electrical and Electronics Engineers -IEEE-, Croatia Section; European Association for Signal Processing -EURASIP-:
IWSSIP 2014, 21st International Conference on Systems, Signals and Image Processing. Proceedings : 12-15 May 2014, Dubrovnik, Croatia
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-2602-2
ISBN: 978-953-184-191-7 (Print)
International Conference on Systems, Signals and Image Processing (IWSSIP) <21, 2014, Dubrovnik>
Conference Paper
Fraunhofer IGD ()
biometrics; biometric template protection; compression; Business Field: Digital society; Research Area: Human Computer Interaction (HCI)

Biometrics provide a source of automated recognition of individuals based on their physiological and behavioral characteristics. As per Directive 95/46/EC, biometric data is considered to be personal data. And according to article 8 of the European Convention on Human Rights, personal data needs to be privacy preserved. Biometric template protection mechanisms provide a privacy preserved biometric authentication. Such mechanisms assist irreversibility, revocability and unlinkability of biometric templates. Recently, a bloom filter based approach was proposed to generate irreversible iris template. In this paper, a helper data scheme for 2D cancelable face verification using bloom filters is proposed. The positions of most representative features (stable features) are used as helper data, which helps in the face recognition. The features used are extracted using Local Binary Linear Discriminant Analysis. The effect of stable features on recognition performance under scenarios of with and without using bloom filters is investigated. In addition, recognition performance after compressing multiple features into a single bloom filter is presented. The results are experimentally proved on two benchmark databases namely LFW and ORL datasets.