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

Thesecurity, lackofanonymity andrevocability ofthebiometric template arecritical issues thatneedtobe addressed inordertovindicate theviability ofbiometric based authentication systems. Several methods havebeenproposed to address these problems. However, mostofthese methods offer loweraccuracies thanthebasesystem wherethetemplate is insecure. Thisisbecause inmostsystems thegaininthesecurity isachieved asaresult oflossinnon-redundant information. Inthispaper, we describe tradeoff between accuracy ofan anonymous system andthesecurity ofthebiometrics system. Asacasestudywestart withahighly secure representation ofafingerprint. Thenwedescribe several methods andshow experimental results proving thateverytimewe addmore information tothesecure representation, theaccuracy increases, however valuable information isrevealed toanadversary. We propose, viaak-trial attack model, howthetrade-off canbe complemented byanother security policy suchasanaccount lockout after agiven numberofattempts. I.INTRODUCTION Biometric systems suchasfingerprint havelimited ac- curacies. Thisisfurther accentuated whenthematching is performed inasecure mannersoasnottoreveal information about theactual biometric. Suchsystems called, anonymous, cancelable orrevocable biometric systems often suffer from loweraccuracies ascompared totheplain counterparts, if anyexist. Inthis paper wedescribe amethod toimprove on theaccuracies ofaregistration freeanonymous fingerprint verification system. We alsodiscuss thesecurity trade-offs madetherein. Theproposed methods havebeentested using areal-time datasetfordifferent cases. InSection IIwepresent abrief discussion oftherecent worksintheareaofanonymous biometrics. Wedescribe ouranonymous fingerprint matching representation inbrief inSection IIIandthemodifications inSection IV.We alsopresent theexperimental analysis inSection IVanddiscuss theprivacy concerns witheach modification inSection V.We drawconclusions andbriefly discuss future research directions inSection VI.

Security andAccuracy Trade-off inAnonymous Fingerprint Recognition

Age progression of a test facial image is facilitated by compiling training data, including a training set(s) having selected initial images of subjects by gender and age-group In addition, the age progression includes manipulating the training data, including: for a given age-group of a training set, substantially aligning respective face shapes; determining a common frame based on the aligned shapes; substantially aligning respective face appearances to generate a shape-free form corresponding to the face appearance of each subject, using the substantially aligned shapes to generate an age-specific shape-dictionary for each age-group, and a common shape-dictionary for the age-groups of the training set, and using the aligned appearances to generate at least an age-specific appearance-dictionary for each age-group, and a common appearance-dictionary for the age-groups of the training set The age specific appearance dictionary for each age group and the common appearance dictionary facilitate age progression of the facial image

Age progression of subject facial image

An object detection method can begin with receipt of an ensemble classifier previously trained upon a binary dataset of images for an object The ensemble classifier can be defined by a list, W, of N weak classifiers, wn for n=1,    , N The ensemble classifier can be trained as a detector for the object using a training dataset of images; each image can have a set of candidates having one or more known ground truth candidates As a result of training, the weak classifiers can be reordered, based upon performance, into a new list, W′, and a rejection threshold on candidate rank can be assigned for each detection stage A cascading algorithm can be performed using the trained detector to detect the object in an image having known object candidates The rejection threshold for a stage of the cascading algorithm can be applied to a rank of the object candidates

Object detection approach using an ensemble strong classifier

A method of displaying information relating to an allergen present in a food substance together with a user profile, the method including capturing an image of a scene, segmenting the image to determining at least a segmentation of the food substance in the image, determining a classification of the food substance using the segmentation, determining a presence of the allergen in the food substance using the classification of the food substance and a database of allergens and food substances, determining a risk to a user using the user profile specifying a user sensitivity to the allergen, and displaying an output including a view of the user profile and the image, wherein the image is augmented to identify the presence of the allergen.

Automatic identification of food substance

This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE). Given a pair of encrypted feature vectors, we perform the following ciphertext operations, i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) scale normalization to unit ℓ2-norm, and iv) match score computation. Our method, dubbed HEFT (Homomorphi-cally Encrypted Fusion of biometric Templates), is custom-designed to overcome the unique constraint imposed by FHE, namely the lack of support for non-arithmetic operations. From an inference perspective, we systematically explore different data packing schemes for computationally efficient linear projection and introduce a polynomial approximation for scale normalization. From a training perspective, we introduce an FHE-aware algorithm for learning the linear projection matrix to mitigate errors induced by approximate normalization. Experimental evaluation for template fusion and matching of face and voice biometrics shows that HEFT (i) improves biometric verification performance by 11.07% and 9.58% AUROC compared to the respective unibiometric representations while compressing the feature vectors by a factor of 16 (512D to 32D), and (ii) fuses a pair of encrypted feature vectors and computes its match score against a gallery of size 1024 in 884 ms. Code and data are available at https://github.com/humananalysis/encrypted-biometric-fusion

Nalini K. Ratha

Papers

Security andAccuracy Trade-off inAnonymous Fingerprint Recognition

Age progression of subject facial image

Object detection approach using an ensemble strong classifier

Automatic identification of food substance

HEFT: Homomorphically Encrypted Fusion of Biometric Templates