ML-DRIVEN PALM PRINT AUTHENTICATION SYSTEM FOR SECURITY APPLICATIONS
Keywords:
Keywords: Palm print authentication, Machine learning, Biometric security, Image preprocessing, Pattern recognition, Extra Trees Classifier.Abstract
Biometric authentication has gained significant traction, with palm print recognition emerging as arobust and reliable security measure. Research indicates that palm print authentication achieves anaccuracy of up to 98.5%, outperforming traditional fingerprint recognition, which averages around 95%.However, conventional manual authentication methods
References
Adjabi, I.; Ouahabi, A.; Benzaoui, A.; Taleb-Ahmed, A. Past, Present, and Future of Face Recognition: A Review. Electronics 2020, 9, 1188.
Ross, A.; Banerjee, S.; Chen, C.; Chowdhury, A.; Mirjalili, V.; Sharma, R.; Yadav, S. Some Research Problems in Biometrics: The Future Beckons. In Proceedings of the 2019 International Conference on Biometrics (ICB), Crete, Greece, 4–7 June 2019; pp. 1–8
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Published
2025-04-10
How to Cite
Burgu Maheshwari, Pasupuleti Chandana, Arsham Arjun, Gandloju Ranadheer, Puppala Rishikesh. (2025). ML-DRIVEN PALM PRINT AUTHENTICATION SYSTEM FOR SECURITY APPLICATIONS. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 99–109. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2278
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