Spectral minutiae representations for fingerprint recognition

The term biometrics refers to the technologies that measure and analyze human intrinsic physical or behavioral characteristics for authenticating individuals. Nowadays, biometric technology is increasingly deployed in civil and commercial applications. The growing use of biometrics is raising security and privacy concerns. Storing biometric data, known as biometric templates, in a database leads to several privacy risks such as identity fraud and cross matching. A solution is to apply biometric template protection techniques, which aim to make it impossible to recover the biometric data from the templates.\ud The goal of our research is to combine biometric systems with template protection. Aimed at fingerprint recognition, this thesis introduces the Spectral Minutiae Representation method, which enables the combination of a minutiae-based fingerprint recognition system with template protection schemes based on fuzzy commitment or helper data schemes.\ud In this thesis, three spectral minutiae representation methods have been proposed: the location-based spectral minutiae representation (SML), the orientation-based spectral minutiae representation (SMO) and the complex spectral minutiae representation (SMC). From the experiments shown in this thesis, SMC achieved the best results.\ud Based on the spectral minutiae features, this thesis further presented contributions in three research directions. First, this thesis recommends several ways to enhance the recognition performance of SMC. Second, with regard to feature reduction, this thesis introduced two feature reduction methods, Column-PCA (CPCA) and Line-DFT (LDFT). Third, with regard to quantization, this thesis introduced the Spectral Bits and Phase Bits representations. \ud The spectral minutiae representation scheme proposed in this thesis enables the combination of fingerprint recognition systems with template protection based on the helper data scheme. Furthermore, this scheme allows for a fast minutiae comparison, which renders this scheme suitable as a pre-selector for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching. The binary spectral minutiae representation achieved an equal error rate of less than 1% on the FVC2000-DB2 database when applying multi-sample enrolment. The fast comparison speed together with the promising recognition performance makes this spectral minutiae scheme very applicable for real time applications

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/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

A Correlation-Based Fingerprint Verification System

In this paper, a correlation-based fingerprint verification system is presented. Unlike the traditional minutiae-based systems, this system directly uses the richer gray-scale information of the fingerprints. The correlation-based fingerprint verification system first selects appropriate templates in the primary fingerprint, uses template matching to locate them in the secondary print, and compares the template positions of both fingerprints. Unlike minutiae-based systems, the correlation-based fingerprint verification system is capable of dealing with bad-quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions. Experiments have shown that the performance of this system at the moment is comparable to the performance of many other fingerprint verification systems