Multi-instance cancelable iris authentication system using triplet loss for deep learning models

Many government and commercial organizations are using biometric authentication systems instead of a password or token-based authentication systems. They are computationally expensive if more users are involved. To overcome this problem, a biometric system can be created and deployed in the cloud which then can be used as a biometric authentication service. Privacy is the major concern with cloud-based authentication services as biometric is irrevocable. Many biometric authentication systems based on cancelable biometrics are developed to solve the privacy concern in the past few years. But the existing methods fail to maintain the trade-off between speed, security, and accuracy. To overcome this, we present a multi-instance cancelable iris system (MICBTDL). MICBTDL uses a convolutional neural network trained using triplet loss for feature extraction and stores the feature vector as a cancelable template. Our system uses an artificial neural network as the comparator module instead of the similarity measures. Experiments are carried on IITD and MMU iris databases to check the effectiveness of MICBTDL. Experimental results demonstrate that MICBTDL accomplishes fair performance when compared to other existing works.