Gender Classification from Fingerprint-images using Deep Learning Approach
Beanbonyka Rim, Junseob Kim, Min Hong
Abstract
Accurate gender classification from fingerprint-images brings benefits to various forensic, security and authentication analysis. Those benefits help to narrow down the space for searching and speed up the process for matching for applications such as automatic fingerprint identification systems (AFIS). However, achieving high prediction accuracy without human intervention (such as preprocessing and hand-crafted feature extraction) is currently and potentially a challenge. Therefore, this paper presents a deep learning method to automatically and conveniently estimate gender from fingerprint-images. In particular, the VGG-19, ResNet-50 and EfficientNet-B3 model were exploited to train from scratch. The raw images of fingerprints were fed into the networks for end-to-end learning. The networks trained on 8,000 images, validated on 1,520 images and tested on 360 images. Our experimental results showed that by comparing between those state-of-the-art models (VGG-19, ResNet-50 and EfficientNet-B3), EfficientNet-B3 model achieved the best accuracy of 97.89%, 69.86% and 63.05% for training, validating, and testing, respectively.