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Article

Application of Convolutional Neural Network for Fingerprint-Based Prediction of Gender, Finger Position, and Height

1
Institute of Biophotonics and Brain Research Center, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
2
Forensic Science Center, New Taipei City Police Department, New Taipei City 22005, Taiwan
3
Department of Neurology, Taipei Municipal Gan-Dau Hospital, Taipei 22360, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Andrea Prati, Luis Javier García Villalba and Vincent A. Cicirello
Entropy 2022, 24(4), 475; https://doi.org/10.3390/e24040475
Received: 12 March 2022 / Revised: 26 March 2022 / Accepted: 27 March 2022 / Published: 29 March 2022
(This article belongs to the Topic Machine and Deep Learning)
Fingerprints are the most common personal identification feature and key evidence for crime scene investigators. The prediction of fingerprints features include gender, height range (tall or short), left or right hand, and finger position can effectively narrow down the list of suspects, increase the speed of comparison, and greatly improve the effectiveness of criminal investigations. In this study, we used three commonly used CNNs (VGG16, Inception-v3, and Resnet50) to perform biometric prediction on 1000 samples, and the results showed that VGG16 achieved the highest accuracy in identifying gender (79.2%), left- and right-hand fingerprints (94.4%), finger position (84.8%), and height range (69.8%, using the ring finger of male participants). In addition, we visualized the CNN classification basis by the Grad-CAM technique and compared the results with those predicted by experts and found that the CNN model outperformed experts in terms of classification accuracy and speed, and provided good reference for fingerprints that were difficult to determine manually. View Full-Text
Keywords: fingerprint recognition; artificial neural network; image classification fingerprint recognition; artificial neural network; image classification
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MDPI and ACS Style

Hsiao, C.-T.; Lin, C.-Y.; Wang, P.-S.; Wu, Y.-T. Application of Convolutional Neural Network for Fingerprint-Based Prediction of Gender, Finger Position, and Height. Entropy 2022, 24, 475. https://doi.org/10.3390/e24040475

AMA Style

Hsiao C-T, Lin C-Y, Wang P-S, Wu Y-T. Application of Convolutional Neural Network for Fingerprint-Based Prediction of Gender, Finger Position, and Height. Entropy. 2022; 24(4):475. https://doi.org/10.3390/e24040475

Chicago/Turabian Style

Hsiao, Chung-Ting, Chun-Yi Lin, Po-Shan Wang, and Yu-Te Wu. 2022. "Application of Convolutional Neural Network for Fingerprint-Based Prediction of Gender, Finger Position, and Height" Entropy 24, no. 4: 475. https://doi.org/10.3390/e24040475

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