Hand classification based on fingerprint using Lightweight Convolutional Neural Network

Fingerprints are recognized as one of the most distinctive and reliable biometric identifiers that play a crucial role in forensic investigations by aiding in the swift identification of individuals. While traditional fingerprint analysis focuses on individual identification, determining the hand from which a particular fingerprint originates holds significant untapped potential. This paper proposes lightweight Convolutional Neural Networks to identify the hand from fingerprints. The model could achieve high accuracy on publicly available fingerprint datasets such as CASIA, SOCOFing, and NISTSD4. An in-depth analysis of the network prediction is conducted to determine the features that help the model identify the hand from the fingerprint. It is found that the position of core point, direction of ridge flow, inter-ridge distance at side ridges, and the slope of the ridges help the model identify the hand from fingerprints.