Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

/pdf/data-clustering-a-review-4c452f5cab.pdf

Data clustering: a review

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

/pdf/handbook-of-fingerprint-recognition-16mh887j24.pdf

Handbook of Fingerprint Recognition

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

/pdf/fingerprint-image-enhancement-algorithm-and-performance-405diava9j.pdf

Fingerprint image enhancement: algorithm and performance evaluation

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

IEEE Transactions on Pattern Analysis and Machine Intelligence

Measuring the accuracy of biometrics systems is important. Accuracy estimates depend very much on the quality of the test data that are used: including poor quality data will degrade the accuracy estimates. Factors that determine the good quality data and poor quality data can not be revealed by simple accuracy estimates. We propose a novel methodology to analyze how the overall accuracy estimate of a system relates to the specific quality of biometrics samples. Using a large collection of fingerprint samples, we present an analysis of system accuracy, which suggests that a significant part of the error is due to few fingers.

Structure in errors: a case study in fingerprint verification

Biometrics-based authentication and identification systems have to handle images acquired in noisy and hostile environments. The signal quality is assessed to decide if there is sufficient signal strength to process further. Poor quality signals require "enhancement" before further processing of the input signal. Often enhancement implies creating a more visibly pleasing image. However, biometrics signals need to improve the image quality for machine processability. This means that the enhancement algorithm should have some weak model about the sample (image) formation process. Enhancement is then some type of "normalization" or "beautification". We present a weak model-based image enhancement algorithm for fingerprint images. The results of the proposed algorithm are presented in terms of the improvements in the overall system performance measured in terms of a receiver operating characteristics curve.

Fingerprint image enhancement using weak models

Method, apparatus and computer program product compare biometrics in an anonymous manner. A first collection of biometrics is transformed using a first cancelable non-invertible biometric transform to create a first collection of transformed biometrics. A second collection of biometrics is transformed using the first cancelable non-invertible biometric transform to create a second collection of transformed biometrics. The first and second collection of transformed biometrics are then compared in the transformed domain to determine if any of the transformed biometrics from the first collection match any of the transformed biometrics from the second collection. If a match is found, the parties respectively maintaining the first and second collections of biometrics exchange information confidential nature of the biometrics are maintained by the entities responsible for the collections, since the biometrics are not compared in an untransformed state.

Method, Apparatus and Computer Program Product Implementing Anonymous Biometric Matching

An example operation may include one or more of obtaining a first biometric sample of a user from a user device. extracting, by an issuing node of a permissioned blockchain network, a biometric template from the first biometric sample, encrypting the biometric template, distributing an issuetoken proposal comprising the encrypted biometric template to the blockchain network, and generating and distributing a biometric token to the user device. In response to the user indicating to the user device to redeem the biometric token, the method includes one or more of presenting, by the user device, the biometric token to a verifying node of the blockchain network, validating, by the verifying node, the biometric token, receiving, by the verifying node, a second biometric sample from the user device, distributing a redeemtoken proposal to the blockchain network, committing a transaction corresponding to the biometric token, to the blockchain network, and invalidating the biometric token.

Biometric token for blockchain

Fingerprint images contain varying amount of noise because of the limitations of the fingerprint acquisition process. It is often necessary to enhance such noisy fingerprint images so that the features extracted from them are reliable. We propose a novel approach to fingerprint enhancement where a set of filters are learned using the "learn-from-example" paradigm. An expert provides the ground truth information for ridges in a small set of representative fingerprint images. The space of local fingerprint patterns in a small neighborhood is partitioned into a set of expressive yet computationally simple classes. A filter is learnt for each partition by finding the optimal linear mapping (in least-square sense) from the input to the enhanced space. The proposed approach offers distinct performance and speed advantages for a wide variety of fingerprint images.

Nalini K. Ratha

Papers

Structure in errors: a case study in fingerprint verification

Fingerprint image enhancement using weak models

Method, Apparatus and Computer Program Product Implementing Anonymous Biometric Matching

Biometric token for blockchain

Learning partitioned least squares filters for fingerprint enhancement