Secure deep multimodal biometric authentication using online signature and face features fusion

Singhal, Manas; Shinghal, Kshitij

doi:10.1007/s11042-023-16683-1

Cited by 8 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is often effective to use a single biometric method; nevertheless, various studies have shown that combining multiple systems produces better results [14,[22][23][24]. Multiple biometrics can be defined as the use of sensors together (multi-sensor) [25], the use of different instances of the same biometric characteristic (multi-instance) [17], or the use of distinct biometric features together (multimodal) [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Salturk,

Kahraman

2024

Neural Comput & Applic

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

“…There have been studies on this topic that use paper signatures [29,30], air signatures [20,21], and time-varying physical conditions when signing [16][17][18]31]. Moreover, there have also been studies in which these biometric techniques have been utilized in tandem [22].…”

Section: Introductionmentioning

confidence: 99%