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

Chung Ting Hsiao, Chun Yi Lin, Po Shan Wang, Yu Te Wu*

*此作品的通信作者

研究成果: Article同行評審

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號475
期刊Entropy
24
發行號4
DOIs
出版狀態Published - 4月 2022

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