Cancelable face and iris recognition system based on deep learning

Biometric recognition is an automated technique of recognising persons based on their traits. Because of their exceptional texture, the biometric features' ostensibly random nature makes them good candidates for recognition. These features are unique for each individual even for identical twins authentication. The latest developments in Deep Learning (DL) and computer vision has proved that Convolutional Neural Networks (CNNs) can extract generic descriptors that can represent complex image features. How to protect the biometric data and ensure user’s privacy is a main concern, nowadays. Hence, several cancelable biometric scenarios have been proposed. In this paper, we propose a novel cancelable biometric recognition system based on a CNN model with bio-convolution. The performance metrics are estimated on different face and iris datasets. In contrary to most conventional secure biometric recognition systems, the proposed system achieves superior accuracy results, while keeping the ability to cancel the biometric traits if compromised. The experimental findings on each database are shown and compared to those of the state-of-the-art systems that have been tested on the same database. Furthermore, the recognition rates reach 99.15%, 98.35%, 97.89, and 95.48% with the LFW, FERET, IITD, and CASIA-IrisV3 databases, respectively.