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

Locality-Sensitive Hashing with Margin Based Feature Selection

We propose a learning method with feature selection for Locality-Sensitive Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays. These bit arrays can be used to perform similarity searches and personal authentication. The proposed method uses bit arrays longer than those used in the end for similarity and other searches and by learning selects the bits that will be used. We demonstrated this method can effectively perform optimization for cases such as fingerprint images with a large number of labels and extremely few data that share the same labels, as well as verifying that it is also effective for natural images, handwritten digits, and speech features.Comment: 9 pages, 6 figures, 3 table

Hyperplane Arrangements and Locality-Sensitive Hashing with Lift

Locality-sensitive hashing converts high-dimensional feature vectors, such as image and speech, into bit arrays and allows high-speed similarity calculation with the Hamming distance. There is a hashing scheme that maps feature vectors to bit arrays depending on the signs of the inner products between feature vectors and the normal vectors of hyperplanes placed in the feature space. This hashing can be seen as a discretization of the feature space by hyperplanes. If labels for data are given, one can determine the hyperplanes by using learning algorithms. However, many proposed learning methods do not consider the hyperplanes' offsets. Not doing so decreases the number of partitioned regions, and the correlation between Hamming distances and Euclidean distances becomes small. In this paper, we propose a lift map that converts learning algorithms without the offsets to the ones that take into account the offsets. With this method, the learning methods without the offsets give the discretizations of spaces as if it takes into account the offsets. For the proposed method, we input several high-dimensional feature data sets and studied the relationship between the statistical characteristics of data, the number of hyperplanes, and the effect of the proposed method.Comment: 9 pages, 7 figure

Novel Feature Extraction Methodology with Evaluation in Artificial Neural Networks Based Fingerprint Recognition System

Fingerprint recognition is one of the most common biometric recognition systems that includes feature extraction and decision modules. In this work, these modules are achieved via artificial neural networks and image processing operations. The aim of the work is to define a new method that requires less computational load and storage capacity, can be an alternative to existing methods, has high fault tolerance, convenient for fraud measures, and is suitable for development. In order to extract the feature points called minutia points of each fingerprint sample, Multilayer Perceptron algorithm is used. Furthermore, the center of the fingerprint is also determined using an improved orientation map. The proposed method gives approximate position information of minutiae points with respect to the core point using a fairly simple, orientation map-based method that provides ease of operation, but with the use of artificial neurons with high fault tolerance, this method has been turned to an advantage. After feature extraction, General Regression Neural Network is used for identification. The system algorithm is evaluated in UPEK and FVC2000 database. The accuracies without rejection of bad images for the database are 95.57% and 91.38% for UPEK and FVC2000 respectively

DPD-DFF: a dual phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases

Nowadays, many companies and institutions need fast and reliable identification systems that are able to deal with very large databases. Fingerprints are among the most used biometric traits for identification. In the current literature there are fingerprint matching algorithms that are focused on efficiency, whilst others are based on accuracy. In this paper we propose a flexible dual phase identification method, called DPD-DFF, that combines two fingers and two matchers within a hybrid fusion scheme to obtain both fast and accurate results. Different alternatives are designed to find a trade-off between runtime and accuracy that can be further tuned with a single parameter. The experiments show that DPD-DFF obtains very competitive results in comparison with the state-of-the-art score fusion techniques, especially when dealing with large databases or impostor fingerprints

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers