Gender Detection and Classification from Fingerprints Using Convolutional Neural Network

The fingerprint is considered an essential biometric modality to detect a human being. This work will help us to investigate the possibility of detecting and classifying gender from human fingerprints. Gender detection and classification significantly reduce the time to investigate criminal offenses and gender imitation. In this proposed study, we use a convolutional neural network (CNN) to identify a human's gender based on their fingerprint. Gender categorization accuracy of 96.47 is achieved using the CNN (Fig-net) architecture. This data is derived from the freely accessible SOCOFing (Sokoto Coventry Fingerprint dataset). To create this algorithm, we relied on the Python 3.6 framework.