Comparison of Deep Learning Models for Biometric-based Mobile User Authentication

Abstract

Recent advances have demonstrated the efficacy of deep learning models for mobile biometrics. In this paper, we evaluate and compare different deep learning models in terms of their efficiency and accuracy when used for biometric user authentication on mobile devices. To this aim, well-known pre-trained architectures such as VGG, ResNet, DenseNet and models proposed for mobile vision applications such as MobileNetVl, MobileNetV2, and NasNet-mobile, are compared with respect to their matching performance and computational cost. Further, we propose a compact and fast custom deep learning model for efficient operations on resource-constrained mobile devices. Experimental investigations were performed as a case study on mobile ocular biometrics using a large-scale VISOB dataset. Reported results suggest the best trade-off between performance and computational cost by our custom model over existing deep learning architectures.