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

Humans, as well as many living organisms, are gifted with the power of "seeing" and Bunderstanding[ the environment around them using their eyes. The ease with which humans process and understand the visual world is very deceiving and often prompts us to underestimate the effort and methods needed to build practical, effective, and inexpensive computer vision systems. In essence, humans have a 500-million-year head start due to evolution; it is extremely difficult at this point to build a computer vision system that has the abilities of a three-year-old child. However, by confining ourselves to particular domains, we can often find shortcuts to solve particular problems. This paper illustrates a number of such solutions in various areas developed by our group at IBM. These include object finding for video surveillance, person identification via biometrics, inspection of manufactured items along railways, and scene understanding for driver assistance, as well as object recognition and motion interpretation for retail stores. We discuss the real-world constraints for each system and describe how we overcame the irksome variability inherent in each task. By further analyzing such successful systems and comparing them to each other, we can come to understand the common underlying problems and thus start to extend our initially limited areas of competence into a more general-purpose vision toolkit. This paper concludes with a set of challenging unresolved problems that if solved could spur great progress in practical computer vision.

/pdf/practical-computer-vision-example-techniques-and-challenges-43xptd1wfp.pdf

Practical computer vision: example techniques and challenges

In the modern electronic world, authentication of a person is an important task in many areas of day-to-day life. Using a biometrics to authenticate a person’s identity has several advantages over the present practices of passwords and/or authentication cards using magnetic stripes or bar codes. However, with the use of a biometrics there is an open issue of misuse of the biometrics for purposes that the owner of the biometrics may not be aware of. In this chapter, we propose a new method of remote authentication that combines the security of a smartcard with the accuracy and convenience of biometrics to authenticate the identity of a person. With this approach, the need to access a large biometrics database is eliminated. The proposed method can be used in many application areas including system security, electronic commerce and access control.

Smartcard Based Authentication

In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn't require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.

/pdf/qplc-a-novel-multimodal-biometric-score-fusion-method-2zmydmfihu.pdf

QPLC: A novel multimodal biometric score fusion method

Audio- and Video-Based Biometric Person Authentication: 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings (Lecture Notes in Computer Science)

A display device comprises a plurality of light emitting elements in a layer on a substrate, a plurality of microprisms positioned over the layer, a plurality of light detectors on the substrate, each light detector respectively corresponding to a light emitting element of the plurality of light emitting elements, and a display screen, wherein the light detectors are used to sense at least one property of an item in contact with the display screen.

Nalini K. Ratha

Papers

Practical computer vision: example techniques and challenges

Smartcard Based Authentication

QPLC: A novel multimodal biometric score fusion method

Audio- and Video-Based Biometric Person Authentication: 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings (Lecture Notes in Computer Science)

Display device including a display screen with integrated imaging and a method of using same