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.

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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

Mechanisms are provided for training a classifier to identify adversarial input data. A neural network processes original input data representing a plurality of non-adversarial original input data and mean output learning logic determines a mean response for each intermediate layer of the neural network based on results of processing the original input data. The neural network processes adversarial input data and layer-wise comparison logic compares, for each intermediate layer of the neural network, a response generated by the intermediate layer based on processing the adversarial input data, to the mean response associated with the intermediate layer, to thereby generate a distance metric for the intermediate layer. The layer-wise comparison logic generates a vector output based on the distance metrics that is used to train a classifier to identify adversarial input data based on responses generated by intermediate layers of the neural network.

Identifying artificial artifacts in input data to detect adversarial attacks

The FBI Wavelet Scalar Quantization (WSQ) compression standard was developed by the US Federal Bureau of Investigation (FBI). The main advantage of WSQ-based fingerprint image compression has been its superiority in preserving the fingerprint minutiae features even at very high compression rates which standard JPEG compression techniques were unable to preserve. With the advent of JPEG 2000 image compression technique based on Wavelet transforms moving away from DCT-based methods, we have been motivated to investigate if the same advantage still persists. In this paper, we describe a set of experiments we carried out to compare the performance of WSQ with JPEG 2000. The performance analysis is based on three public databases of fingerprint images acquired using different imaging sensors. Our analysis shows that JPEG 2000 provides better compression with less impact on the overall system accuracy performance.

A comparative performance analysis of JPEG 2000 vs. WSQ for fingerprint image compression

PROBLEM TO BE SOLVED: To provide a system and a method for authenticating a user by a radio communication, using an acquired bio-metric (for example fingerprint) and a bio-metric template stored locally SOLUTION: A smart card system used for bio-metric authentication is slow in a processing speed, and the card itself has additionally demerits of erroneous storage or loss A problem of security of the possibility exposed to risk is exhibited in storage (in a database) of a bio-metric data via a network High security is attained when the bio-metric template is stored locally in a portable device The user uses the portable device to radio-transmit the bio-metric stored for the authentication, or measures locally the bio-metric using the portable device to collated with bio-metric stored locally (in the portable device)

Method for bio-metric-based authentication in radio communication for access control

While biometrics-based identification is a key technology in many critical applications such as searching for an identity in a watch list or checking for duplicates in a citizen ID card system, there are many technical challenges in building a solution because the size of the database can be very large (often in 100s of millions) and the intrinsic errors with the underlying biometrics engines. Often multi-modal biometrics is proposed as a way to improve the underlying biometrics accuracy performance. In this paper, we propose a score based fusion scheme tailored for identification applications. The proposed algorithm uses a gradient descent method to learn weights for each modality such that weighted sum of genuine scores is larger than the weighted sum of all the impostor scores. During the identification phase, top K candidates from each modality are retrieved and a super-set of identities is constructed. Using the learnt weights, we compute the weighted score for all the candidates in the superset. The highest scoring candidate is declared as the top candidate for identification. The proposed algorithm has been tested using NIST BSSR-1 dataset and results in terms of accuracy as well as the speed (execution time) are shown to be far superior than the published results on this dataset.

A Gradient Descent Approach for Multi-modal Biometric Identification

Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for cross-database experiments.

Nalini K. Ratha

Papers

Identifying artificial artifacts in input data to detect adversarial attacks

A comparative performance analysis of JPEG 2000 vs. WSQ for fingerprint image compression

Method for bio-metric-based authentication in radio communication for access control

A Gradient Descent Approach for Multi-modal Biometric Identification

Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition.