Introduction to Presentation Attacks in Signature Biometrics and Recent Advances

González-García, Carlos; Tolosana, Rubén; Vera-Rodríguez, Rubén; Fiérrez, Julián; Ortega‐Garcia, Javier

doi:10.1007/978-981-19-5288-3_16

Cited by 1 publication

(1 citation statement)

References 54 publications

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“…In their work, Khan et al [10] built an architecture based on Gaussian gated recurrent unit (GGRU) tailored for handwritten signature biometrics. Focusing on potential vulnerabilities in signature biometrics, Gonzalez-Garcia et al [11] investigated various attack scenarios. Jain et al [12] took a different approach by integrating geometrical features and neural networks for signature verification.…”

Section: Introductionmentioning

confidence: 99%

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

Salturk,

Kahraman

2024

Neural Comput & Applic

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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.

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Section: Introductionmentioning

confidence: 99%