Neural Network-Powered Finger-Drawn Biometric Authentication

TL;DR

This study explores neural network-based biometric authentication using finger-drawn digits on touchscreens, comparing CNN and autoencoder models for accuracy and efficiency, demonstrating the viability of finger-drawn symbols as a secure biometric method.

Contribution

It introduces and evaluates CNN and autoencoder architectures specifically designed for finger-drawn digit authentication on mobile devices, highlighting their effectiveness and efficiency.

Findings

CNN achieved ~89% accuracy in user authentication

Autoencoders achieved ~75% accuracy

Shallow CNN required fewer parameters for mobile deployment

Abstract

This paper investigates neural network-based biometric authentication using finger-drawn digits on touchscreen devices. We evaluated CNN and autoencoder architectures for user authentication through simple digit patterns (0-9) traced with finger input. Twenty participants contributed 2,000 finger-drawn digits each on personal touchscreen devices. We compared two CNN architectures: a modified Inception-V1 network and a lightweight shallow CNN for mobile environments. Additionally, we examined Convolutional and Fully Connected autoencoders for anomaly detection. Both CNN architectures achieved ~89% authentication accuracy, with the shallow CNN requiring fewer parameters. Autoencoder approaches achieved ~75% accuracy. The results demonstrate that finger-drawn symbol authentication provides a viable, secure, and user-friendly biometric solution for touchscreen devices. This approach can be integrated with existing pattern-based authentication methods to create multi-layered security systems for mobile applications.