Multi-instance Cancelable Biometric System using Convolutional Neural Network
Tanuja Sudhakar, Marina L. Gavrilova · 2019
Cancelable or Revocable biometrics is a recent trend to safeguard a biometric system from a variety of attacks. In this paper, we propose a cancelable system in which iris features are extracted through deep learning and then converted into a cancelable biometric template through random projection method. We then adopt another machine learning algorithm - Support Vector Machine for optimal biometric authentication after performing comparitive analysis over 3 alternative classifiers. The proposed system provides better template security and improves identification accuracy. As per our knowledge, this combination of deep learning and random projection technique has been employed for the first time. The paper presents an extensive validation of the proposed methodology on two multi-instance iris databases.