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Towards Securing Biometric Templates using Self Generated Dynamic Helper Data

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Academic year: 2017

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Fig. 1. Traditional biometric system. Biometric samples are collected and converted into biometric templates
Fig. 2. Proposed system with the encoder which combines stored learned statistical representations and chaotic mixing and with the decoder which combines blind source separation with hidden Markov model.
Fig. 3. Generation of dynamic helper data. From the central iris region maximally consistent keys are generated
Fig. 6. Decoder Enrolment. This is achieved through supervised Hidden Markov Model (HMM) training
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