Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNet

1. Introduction

Automatic fingerprint recognition is one of the most widely studied topic in biometrics over the past 50 years [12]. One of the main challenges in fingerprint recognition is to increase the recognition accuracy, especially for latent fingerprints. Fingerprint comparison is primarily based on minutiae set comparison [28], [11]. A number of hand-crafted approaches [10], [28] have been used to augment the minutiae with their attributes to improve the recognition accuracy. However, robust automatic fingerprint minutiae extraction, particularly for noisy fingerprint images, continues to be a bottleneck in fingerprint recognition systems. Figure 1.

Minutiae detection by the proposed approach on two latent fingerprint images (#7 and #39) from the NIST sd27 dataset [6]. Left column: Minutiae score maps obtained from the latent images shown in the right column. Right column: Minutiae detected by the proposed framework (red) and ground truth minutiae (blue) overlaid on the latent image.