AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A robust multimodal biometric authentication framework that integrates facial and speech modalities at the feature level to overcome the limitations of unimodal systems in terms of accuracy and spoof resistance is proposed.
A novel, integrated security frame- work that forges robust and persistent cryptographic link by embedding a patient’s encrypted fingerprint data directly into their medical images, thereby pre- serving the clinical reliability of the scan.
Multimodal biometric recognition integrates complementary information from different modalities and significantly improves recognition accuracy and system security compared with unimodal methods. However, with the widespread adoption of these systems, the risks of biometric template leakage and theft have emerged, posi...
This work proposes a multimodal biometric framework that combines fingerprint and finger vein recognition for identity authentication that requires 156.2 ms per authentication request and about 24 % of CPU on average on a machine with no GPU, which confirms that it can operate in environments with limited computational...
A new multimodal biometric authentication model for providing recognition using fingerprint and finger vein images and a SERA-based SqueezeNet+DNFN model demonstrates strong performance across multiple evaluation metrics.
This paper presents a secure biometric authentication method that integrates homomorphic encryption, fuzzy-based techniques, and classical cryptographic algorithms to ensure data confidentiality and high recognition accuracy. The proposed approach addresses the limitations of traditional systems, which require access t...
Findings indicate that feature-level fusion of pretrained CNNs offers an accurate, explainable, and lightweight solution for biometric patient identification, though larger, diverse datasets and anti-spoofing measures are needed prior to clinical adoption.
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