Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

Khan, Sameera; Singh, Dileep Kumar; Singh, Mahesh; Mena, Desta Faltaso

doi:10.1049/2023/5087083

Cited by 2 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, numerous studies in the literature have delved into the utilization of signature data for person verification and recognition. 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.…”

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

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%

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

show abstract

Beyond the Fingerprint: Exploring the Bibliometric Landscape of Emerging Synthetic Biometrics with a Focus on Synthetic Signatures

Khan,

2024

IJMTS

View full text Add to dashboard Cite

Purpose: This paper aims to conduct a comprehensive bibliometric analysis focusing on synthetic biometrics, particularly synthetic signatures, as an emerging alternative to traditional biometric methods such as fingerprints and iris scans. The study explores the research landscape to identify trends, influential studies, and emerging technologies in this innovative field. Methodology: The research employs bibliometric analysis techniques to systematically map the academic discourse surrounding synthetic biometrics, with a specific emphasis on synthetic signatures. Bibliometric methods include citation analysis, co-citation analysis, and cluster analysis to uncover patterns, connections, and trends within the research literature. These methods provide a retrospective insight into the progression of synthetic signature research and its scholarly impact. Findings/Results:The findings reveal several key insights: Emerging Trends: Synthetic signatures are increasingly recognized for their potential in enhancing security and identification systems beyond traditional biometrics. Influential Studies: Specific studies and authors are identified as pivotal in shaping the discourse and development of synthetic signature technologies. Technological Advancements: The analysis highlights advancements in synthetic biometrics, showcasing how these technologies are evolving to meet the challenges of robust and adaptable verification methods. Challenges and Opportunities: The study identifies both potential and challenges associated with synthetic signatures, providing a nuanced understanding of their feasibility and application in real-world scenarios. Originality/Value: This paper contributes to the existing literature by offering a structured analysis of the bibliometric landscape of synthetic biometrics, particularly synthetic signatures. By mapping out the academic discourse and identifying key trends and technologies, it provides valuable insights into the potential and challenges of this innovative field. The findings are expected to guide future research directions and policy considerations in the development and adoption of synthetic biometric technologies. Paper Type: Review Paper.

show abstract

Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

Cited by 2 publications

References 39 publications

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

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

Beyond the Fingerprint: Exploring the Bibliometric Landscape of Emerging Synthetic Biometrics with a Focus on Synthetic Signatures

Contact Info

Product

Resources

About