Detailed Identification of Fingerprints Using Convolutional Neural Networks

Abstract

Fingerprints, as one of the most widely used biometric modalities, can be used to identify and distinguish between genders. Gender classification is very important in reducing the time when investigating criminal offenders and gender impersonation. In this work, we use deep Convolutional Neural Networks (CNNs) to not only classify fingerprints by gender, but also identify individual hands and fingers. Transfer learning is employed to speed up the training of the CNN. The CNN achieves an accuracy of 75.2%, 93.5%, and 76.72% for the classification of gender, hand, and fingers, respectively. These results obtained using our publicly available Sokoto Coventry Fingerprint Dataset (SOCOFing) serve as benchmark classification results on this dataset.