Enhancing Fingerprint Forensics: A Comprehensive Study of Gender Classification Based on Advanced Data-Centric AI Approaches and Multi-Database Analysis

Abstract

Fingerprint analysis has long been a cornerstone in criminal investigations for suspect identification. Beyond this conventional role, recent efforts have aimed to extract additional demographic information from fingerprints, such as gender, age, and nationality. While studies have demonstrated promising accuracy in gender classification based on fingerprints, practical implementation faces challenges, including the often low quality of crime scene fingerprints. This study presents a pioneering comparison of gender classification across diverse datasets, considering variations in fingerprint image quality. We examine the results from four databases, encompassing both public and private sources, employing state-of-the-art Data-Centric AI (DCAI) approaches for enhanced classification. Our findings reveal that a conservative Convolutional Neural Network (CNN)—specifically VGG—proves effective, achieving an accuracy ranging from 70% to 95% based on fingerprint quality. DCAI methods contribute a noteworthy 1–4% improvement. Notably, for partial or low-quality fingerprints, the periphery emerges as a critical determinant of gender classification. This study contributes insights into practical gender classification from fingerprints, emphasizing the significance of the fingerprint periphery. Furthermore, we provide the source code for future research and accessibility in real-world applications.