PalmNet: A CNN Transfer Learning Approach for Recognition of Young Children Using Contactless Palmprints

Rajaram, Kanchana; Devi, A.Sangeerani; Selvakumar, S.

doi:10.1007/978-981-16-7996-4_44

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…However, the unique characteristics of children's fingerprints can make them difficult to identify using traditional fingerprint recognition methods accurately. Deep learning techniques improve the accuracy of children's fingerprint recognition, explicitly focusing on the ability to distinguish between different children's fingerprints [1][2][3]. Fingerprint recognition is a biometric identification technique that uses an individual's fingerprints' unique patterns to authenticate their identity.…”

Section: Introductionmentioning

confidence: 99%

Enhancing child safety with accurate fingerprint identification using deep learning technology

Sahy¹,

Niu²,

Khalaf³

et al. 2023

PEN

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Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children.

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Section: Introductionmentioning

confidence: 99%