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

The system and method obscures descriptive image information about one or more images. The system comprises a selector for selecting the descriptive image information from one or more of the images, a transformer that transforms the descriptive information into a transformed state, and an authorizer that provides authorization criteria with the image. In a preferred embodiment, the transformed state is the respective image encoded with the descriptive information. The descriptive information can be obscured so that the descriptive information in the transformed state can be decoded only if one or more authorization inputs satisfy the authorization criteria.

Privacy management in imaging system

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is significantly worse than on any other group. We provide initial evidence that skin type alone is not the driver for this disparity by conducting novel stability experiments that vary an image's skin type via color-theoretic methods, namely luminance mode-shift and optimal transport. We evaluate the effect of skin type change on the gender classification decision of a pair of state-of-the-art commercial and open-source gender classifiers. The results raise the possibility that broader differences in ethnicity, as opposed to the skin type alone, are what contribute to unequal gender classification accuracy in face images.

/pdf/color-theoretic-experiments-to-understand-unequal-gender-fxbuzx51z6.pdf

Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy From Face Images

A system and method for generating a cancelable biometric includes providing at least one pattern and combining the at least one pattern with a biometric image by employing a transform pixel operation to generate a transformed enrollment such that information about the transform pixel operation and the at least one pattern form a security key for protecting the biometric image. The biometric image is reused to generate another security key in the event that the security key is to be canceled.

Salting system and method for cancelable iris biometric

We describe the design and synthesis of a high-performance coprocessor for point pattern matching with application to fingerprint matching using Splash 2-an attached processor for SUN SPARCstation hosts. Each of the field programmable gate array (FPGA)-based processing elements (PEs) is programmed using VHDL behavioral modeling. Using the simulation tools, the program logic is verified. The final control bit stream for the PEs is generated using the synthesis tools. The point feature matching coprocessor can run at a peak speed of 17.1 MHz per feature vector of a fingerprint. With 65 features per fingerprint, the matching speed has been projected at the rate of 2.6*10/sup 5/ fingerprints/sec. The synthesized coprocessor was tested on a 10000 fingerprint database.

/pdf/an-fpga-based-point-pattern-matching-processor-with-3hd6rwg92g.pdf

An FPGA-based point pattern matching processor with application to fingerprint matching

Blockchain has emerged as a leading technology that ensures security in a distributed framework. Recently, it has been shown that blockchain can be used to convert traditional blocks of any deep learning models into secure systems. In this research, we model a trained biometric recognition system in an architecture which leverages the blockchain technology to provide fault tolerant access in a distributed environment. The advantage of the proposed approach is that tampering in one particular component alerts the whole system and helps in easy identification of ‘any’ possible alteration. Experimentally, with different biometric modalities, we have shown that the proposed approach provides security to both deep learning model and the biometric template.

Nalini K. Ratha

Papers

Privacy management in imaging system

Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy From Face Images

Salting system and method for cancelable iris biometric

An FPGA-based point pattern matching processor with application to fingerprint matching

Securing CNN Model and Biometric Template using Blockchain