Haiyun Xu

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.

Spectral minutiae: A fixed-length representation of a minutiae set

Minutiae, which are the endpoints and bifurcations of fingerprint ridges, allow a very discriminative classification of fingerprints. However, a minutiae set is an unordered set and the minutiae locations suffer from various deformations such as translation, rotation and scaling. In this paper, we introduce 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. By applying the spectral minutiae representation, we can combine the fingerprint recognition system with a template protection scheme, which requires a fixed-length feature vector. This paper also presents two spectral minutiae matching algorithms and shows experimental results.

Binary Representations of Fingerprint Spectral Minutiae Features

A fixed-length binary representation of a fingerprint has the advantages of a fast operation and a small template storage. For many biometric template protection schemes, a binary string is also required as input. The spectral minutiae representation is a method to represent a minutiae set as a fixed-length real-valued feature vector. In order to be able to apply the spectral minutiae representation with a template protection scheme, we introduce two novel methods to quantize the spectral minutiae features into binary strings: Spectral Bits and Phase Bits. The experiments on the FVC2002 database show that the binary representations can even outperformed the spectral minutiae real-valued features.

A Fast Minutiae-Based Fingerprint Recognition System

The spectral minutiae representation is a 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 as an input a fixed-length feature vector. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%. With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system and shows a matching speed with 125 000 comparisons per second on a PC with Intel Pentium D processor 2.80 GHz and 1 GB of RAM. This fast operation renders our system suitable as a preselector for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems operating at geographical level (e.g., police patrolling) or in complex critical environments (e.g., airports).

Complex spectral minutiae representation for fingerprint recognition

The spectral minutiae representation is designed for combining fingerprint recognition with template protection. This puts several constraints to the fingerprint recognition system: first, no relative alignment of two fingerprints is allowed due to the encrypted storage; second, a fixed-length feature vector is required as input of template protection schemes. The spectral minutiae representation represents a minutiae set as a fixed-length feature vector, which is invariant to translation, rotation and scaling. These characteristics enable the combination of fingerprint recognition systems with template protection schemes and allow for fast minutiae-based matching as well. In this paper, we introduce the complex spectral minutiae representation (SMC): a spectral representation of a minitiae set, as the location-based and the orientation-based spectral minutiae representations (SML and SMO), but it encodes minutiae orientations differently. SMC improves the recognition accuracy, expressed in term of the Equal Error Rate, about 2–4 times compared with SML and SMO. In addition, the paper presents two feature reduction algorithms: the Column-PCA and the Line-DFT feature reductions, which achieve a template size reduction around 90% and results in a 10–15 times higher matching speed (with 125,000 comparisons per second).

Performance Evaluation of Fusing Protected Fingerprint Minutiae Templates on the Decision Level

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

Regional fusion for high-resolution palmprint recognition using spectral minutiae representation

The spectral minutiae representation (SMC) has been recently proposed as a novel method to minutiae-based fingerprint recognition, which is invariant to minutiae translation and rotation and presents low computational complexity. As high-resolution palmprint recognition is also mainly based on minutiae sets, SMC has been applied to palmprints and used in full-to-full palmprint matching. However, the performance of that approach was still limited. As one of the main reasons for this is the much bigger size of a palmprint compared with a fingerprint, the authors propose a division of the palmprint into smaller regions. Then, to further improve the performance of spectral minutiae-based palmprint matching, in this work the authors present anatomically inspired regional fusion while using SMC for palmprints. Firstly, the authors consider three regions of the palm, namely interdigital, thenar and hypothenar, which have inspiration in anatomic cues. Then, the authors apply SMC to region-to-region palmprint comparison and study regional discriminability when using the method. After that, the authors implement regional fusion at score level by combining the scores of different regional comparisons in the palm with two fusion methods, that is, sum rule and logistic regression. The authors evaluate region-to-region comparison and regional fusion based on spectral minutiae matching on a public high-resolution palmprint database, THUPALMLAB. Both manual segmentation and automatic segmentation are performed to obtain the three palm regions for each palm. Essentially using the complex SMC, the authors obtain results on region-to-region comparison which show that the hypothenar and interdigital regions outperform the thenar region. More importantly, the authors achieve significant performance improvements by regional fusion using regions segmented both manually and automatically. One main advantage of the approach the authors took is that human examiners can segment the palm into the three regions without prior knowledge of the system, which makes the segmentation process easy to be incorporated in protocols such as in forensic science.

Decision Level Fusion of Fingerprint Minutiae Based Pseudonymous Identifiers

In a biometric template protected authentication system, a pseudonymous identifier is the part of a protected biometric template that can be compared directly against other pseudonymous identifiers. Each compared pair of pseudonymous identifiers results in a verification decision testing whether both attributes are derived from the same individual. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent, degradation in biometric performance. Therefore fusion is a promising method to enhance the biometric performance in template protected systems. Compared to feature level fusion and score level fusion, decision level fusion exhibits not only the least fusion complexity, but also the maximum interoperability across different biometric features, systems based on scores, and even individual algorithms. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several scenarios (multi-sample, multi-instance, multi- sensor, and multi-algorithm) when fusion is performed on binary decisions obtained from verification of fingerprint minutiae based pseudonymous identifiers. We demonstrate the influence on biometric performance from decision level fusion in different fusion scenarios on a multi-sensor fingerprint database.

Spectral Representations of Fingerprint Minutiae Subsets

The investigation of the privacy protection of biometric templates gains more and more attention. The spectral minutiae representation 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 as an input a fixed-length feature vector. However, the limited overlap of a fingerprint pair can reduce the performance of the spectral minutiae representation algorithm. Therefore, in this paper, we introduce the spectral representations of fingerprint minutiae subsets to cope with the limited overlap problem. In the experiment, we improve the recognition performance from 0.32% to 0.12% in equal error rate after applying the spectral representations of minutiae subsets algorithm.

On the use of spectral minutiae in high-resolution palmprint recognition

The spectral minutiae representation has been proposed as a novel method to minutiae-based fingerprint recognition, which can handle minutiae translation and rotation and improve matching speed. As high-resolution palmprint recognition is also mainly based on minutiae sets, we apply spectral minutiae representation to palmprints and implement spectral minutiae based matching. We optimize key parameters for the method by experimental study on the characteristics of spectral minutiae using both fingerprints and palmprints. However, experimental results show that spectral minutiae representation has much worse performance for palmprints than that for fingerprints. EER 15.89% and 14.2% are achieved on the public high-resolution palmprint database THUPALMLAB using location-based spectral minutiae representation (SML) and the complex spectral minutiae representation (SMC) respectively while 5.1% and 3.05% on FVC2002 DB2A fingerprint database. Based on statistical analysis, we find the worse performance for palmprints mainly due to larger non-linear distortion and much larger number of minutiae.