Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security


Saltürk S., Kahraman N.

NEURAL COMPUTING AND APPLICATIONS, vol.36, no.19, pp.11311-11322, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 19
  • Publication Date: 2024
  • Doi Number: 10.1007/s00521-024-09690-2
  • Journal Name: NEURAL COMPUTING AND APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.11311-11322
  • Keywords: Data fusion, Dynamic biometric data, Multimodal biometric authentication, Signature in the air
  • Yıldız Technical University Affiliated: Yes

Abstract

The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.