Convolutional Neural Network with Biologically Inspired ON/OFF ReLU

Kim, Jonghong; Kim, Seonggyu

doi:10.1007/978-3-319-26561-2_38

Cited by 8 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Children's fingerprints are generally smoother and less defined than adults, making it challenging to extract the necessary features for accurate identification. In addition, children's fingerprints are constantly changing as they grow and develop, further complicating the identification process [6][7]. Deep learning is a kind of artificial intelligence that use neural networks to learn and extract characteristics from data automatically.…”

Section: Introductionmentioning

confidence: 99%

Enhancing child safety with accurate fingerprint identification using deep learning technology

Sahy¹,

Niu²,

Khalaf³

et al. 2023

PEN

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%