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

Privacy Enhancements for Inexact Biometric Templates

Iris biometric entails using the patterns on the iris as a biometric for personal authentication. It has additional benefits over contact-based biometrics such as fingerprints and hand geometry. However, iris biometric often suffers from the following three challenges: ability to handle unconstrained acquisition, privacy enhancement without compromising security, and robust matching. This chapter discusses a unified framework based on sparse representations and random projections that can address these issues simultaneously. Furthermore, recognition from iris videos as well as generation of cancelable iris templates for enhancing the privacy and security is also discussed.

Robust and Secure Iris Recognition

A system and method for generating a cancelable biometric includes shifting at least one pixel region in a biometric image comprised of pixel regions. The at least one pixel region is combined with at least one other pixel region to form a replacement region for the at least one pixel region to form a transformed image. The biometric image is reused to generate another transformed image if the transformed image is to be canceled.

Registration-free transforms for cancelable iris biometrics

One or more processors generate a set of facial appearance parameters that are derived from a first facial image One or more processors generate a graphics control vector based, at least in part, on the set of facial appearance parameters One or more processors render a second facial image based on the graphics control vector One or more processors compare the second facial image to the first image One or more processors generate an adjusted vector by adjusting one or more parameters of the graphics control vector such that a degree of similarity between the second facial image and the first facial image is increased The adjusted vector includes a biometric portion One or more processors generate a first face representation based, at least in part, on the biometric portion of the adjusted vector

Facial analysis by synthesis and biometric matching

Biologically inspired model (BIM) is proven to be an effective feature representation approach for visual object categorization. In BIM, two successive S(simple)-to-C(complex) hierarchical layers are performed to simulate the visual perception process of primate visual cortex. However, the intensive computational cost above C1 layer in BIM extremely limits its application in real-time object recognition tasks. This paper proposes to use a set of improved early biologically inspired features (EBIF, including S1 and C1) for face recognition, in which pyramidal statistics of mean and standard deviation rather than MAX pooling are used for scale-tolerant feature condensation and local normalization is performed on C1 layer. Incremental PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are then combined to efficiently learn a discriminant subspace for feature dimensionality reduction. In the matching stage, Cosine similarity is adopted as the distance metric for a given face pair. Experimental results on two public face datasets and a mobile face dataset show the effectiveness of the proposed method.

Nalini K. Ratha

Papers

Privacy Enhancements for Inexact Biometric Templates

Robust and Secure Iris Recognition

Registration-free transforms for cancelable iris biometrics

Facial analysis by synthesis and biometric matching

Face recognition using early biologically inspired features