Next Generation Palm vein Recognition: Multimodal Fusion and Privacy Preserving Architechtures | IJET – Volume 12 Issue 2 | IJET-V12I2P184

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International Journal of Engineering and Techniques (IJET)

Open Access • Peer Reviewed • High Citation & Impact Factor • ISSN: 2395-1303

Volume 12, Issue 2  |  Published: April 2026

Author: Nidadavolu Haritha

DOI: https://doi.org/{{doi}}  â€˘  PDF: Download

Abstract

Palm vein recognition is the new era of biometric authentication systems.They have gained so much popularity during the recent years because of their speciality and uniqueness. The systems we have now still have some problems like scalability,being able to handle a lot of people at once and spoofing,stopping people from pretending to be someone else and keeping peoples personal information private.The present paper focuses on the idea of creating a multi modal fusion system that integrates palm vein recognition, fingerprint recognition and iris recognition together. Advanced Deep Learning methods are used for feature extraction.The system protects peoples privacy with special privacy techniques like cancelable biometrics, homomorphic encryption and blockchain logging. When we tested our system using a set of data called the CASIA dataset and some data we collected ourselves we found that the system has given 98.7% Accuracy of the time and had an Equal Error rate of 1.6%. Comparitive analysis has shown that the system is better than unimodal systems that only use one method for identification. The fusion system combined the different ways of identification methods together with ways of protecting peoples privacy. This makes the system accurate, secure and fair. This system would be really useful in areas like healthcare, banking and border control, where palm vein recognition and other biometric methods, like fingerprint and iris recognition are used.

Keywords

Palm vein recognition; Multimodal biometrics; Deep learning; Privacy‑preserving architectures; Template protection; Homomorphic encryption; Blockchain; Equal Error Rate (EER); Spoofing resistance; Biometric security

Conclusion

This study presented a system that uses types of biometric data, including palm vein, fingerprint and iris data and adds modules that protect peoples privacy. By using types of biometric data together the system works better than systems that use just one type of biometric data. The system is accurate with an accuracy rate of 98.7 percent and an error rate of 1.6 percent. The modules that protect peoples privacy make sure that the system meets the standards for ethics. The results of the experiment show that the system balances security, robustness and user convenience making it a good choice for use in areas like healthcare, banking and border control.

References

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APA
Nidadavolu Haritha (April 2026). Next Generation Palm vein Recognition: Multimodal Fusion and Privacy Preserving Architechtures. International Journal of Engineering and Techniques (IJET), 12(2). https://doi.org/{{doi}}
Nidadavolu Haritha, “Next Generation Palm vein Recognition: Multimodal Fusion and Privacy Preserving Architechtures,” International Journal of Engineering and Techniques (IJET), vol. 12, no. 2, April 2026, doi: {{doi}}.
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