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

ABSTRACT: 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 classifica

SUBMITTER: Hsiao CT 

PROVIDER: S-EPMC9029985 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature