Neural Network-Powered Finger-Drawn Biometric Authentication

• URL: http://arxiv.org/abs/2511.11235v1
• Date: Fri, 14 Nov 2025 12:39:57 GMT
• Title: Neural Network-Powered Finger-Drawn Biometric Authentication
• Authors: Maan Al Balkhi, Kordian Gontarska, Marko Harasic, Adrian Paschke,
• Abstract summary: This paper investigates neural network-based biometric authentication using finger-drawn digits on touchscreen devices.<n>We evaluated CNN and autoencoder architectures for user authentication through simple digit patterns traced with finger input.<n>The results demonstrate that finger-drawn symbol authentication provides a viable, secure, and user-friendly biometric solution for touchscreen devices.
• Score: 0.9624643581968987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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