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

A face clustering system for video face clustering in a video sequence, the system including an inherent supervision summarization device configured to collect group-level supervision and instance level supervision within a same chunklet based on a user input of face images for a person, a discriminative projection learning device configured to embed group constraints of the group-level supervision into a transformed space, and configured to generate an embedding space from the original image feature space, and a clustering device, in the embedding space, configured to execute pair-wise based clustering to cluster the video images into different clusters with the instance level supervision collected by the inherent supervision summarization device.

Video face clustering detection with inherent and weak supervision

Ein System und Verfahren zur Erzeugung kompakter Irisdarstellungen auf der Grundlage einer Datenbank (502) von Irisbildern beinhalten die Bereitstellung von Iriscodes voller Lange (504) fur Irisbilder in einer Datenbank, wobei der Iriscode voller Lange eine Vielzahl von Teilen enthalt, die Umfangsringen in einem dazugehorigen Irisbild entsprechen. Fur die Iriscodes voller Lange werden mit Hilfe eines Prozessors (512) die Verteilungen des Wahr-Werts und des Falsch-Werts berechnet, und die Codeteile (514) werden identifiziert, die einen Beitrag aufweisen, der eine Trennung zwischen den Falsch- und Wahr-Verteilungen in Bezug auf einen Schwellenwert bereitstellt. Eine Korrelation zwischen den ubrigen Codeteilen wird gemessen. Eine Teilmenge von Codeteilen mit niedrigen Korrelationen innerhalb der Teilmenge wird gebildet, um eine kompakte Irisdarstellung zu erzeugen.

System und Verfahren zur Erzeugung und Verwendung von Iriscodes geringer Länge

Chapter 11 Large-Scale Biometric Identification: Challenges and Solutions

It is becoming increasingly clear that it is humanly impossible to analyze a deluge of data from cameras and other sensors in a variety of applications including surveillance, railroad inspection, driver assistance. The practical systems that we built, although in pursuit of different business objectives, share a common goal, which is to intelligently and efficiently analyze and extract the most important actionable information from an overwhelming amount of data, while being able to effectively ignore a large portions of uneventful and/or noisy data. This presentation will focus on computer vision, machine learning and system optimization techniques that we used to successfully address different technical and business challenges, and to deliver differentiating performance to meet our customers' expectations.

http://ieeexplore.ieee.org/iel7/6459621/6468717/06468725.pdf

Vision-based integrated systems for intelligent data analysis

It is with great pleasure that we welcome Dr. Stephanie Schuckers and Dr. Chang Wen Chen as T-BIOM Associate Editor-in-Chief. Their addition greatly enhances the depth of our editorial board. 

Nalini K. Ratha

Papers

Video face clustering detection with inherent and weak supervision

System und Verfahren zur Erzeugung und Verwendung von Iriscodes geringer Länge

Chapter 11 Large-Scale Biometric Identification: Challenges and Solutions

Vision-based integrated systems for intelligent data analysis

Introduction of New Associate Editors in Chief