Can a CNN Automatically Learn the Significance of Minutiae Points for Fingerprint Matching

Most automated fingerprint recognition systems use minutiae points for comparing fingerprints. In the parlance of Computer Vision, minutiae can be viewed as handcrafted features, i.e., features that have been proposed by human experts for the task of fingerprint recognition. In this work, we raise the following question: Can a machine learning system automatically determine the significance of minutiae points for fingerprint matching? To this effect, a patch-based Siamese Convolutional Neural Network (CNN), which does not explicitly rely on the extraction of minutiae points, is designed and trained from scratch. The purpose of this network is to learn the most effective features for matching fingerprint images. The features learned by this network are analyzed using Gradient-weighted Class Activation Mapping (Grad-CAM) to determine if they correlate with the locations of minutiae points. Our experiments suggest that the proposed network automatically learns to focus on minutiae points, when available, for fingerprint matching. Thus, an automated learner without any explicit domain knowledge establishes the significance of minutiae points for fingerprint matching.