Privacy Preserving Ear Recognition System Using Transfer Learning in Industry 4.0

Abstract

This paper presents an Industry 4.0 compliant ear biometric recognition technique using DenseNet, a well-known convolutional neural network model. Compared to other biometric traits, ear recognition has been a challenge due to the unavailability of a large number of images and, therefore, the improvements due to deep learning application are still unexplored. Additionally ear biometrics has the natural advantage of privacy preservation through excellent feature encoding which is not yet explored. In this article, the performance of DenseNet is initially tested on typically challenging benchmarks such as SVHN, CIFAR and ImageNet, achieving state-of-the-art results and requiring minimal computation time and memory. All the experiments are performed on six popular ear databases namely AMI, AWE, Extended AWE (AWE-X), CVLE, IITD, and WPUT, indicating that the proposed algorithm achieves better performance over state-of-the-art. Due to less trainable parameters and fast processing, this Industry 4.0 compliant proposed recognition method can be widely used over Internet of Biometric Things, ensuring the privacy preservation.