A pilot study on discriminative power of features of superficial venous pattern in the hand

The goal of the project is to develop an automatic way to identify, represent the superficial vasculature of the back hand and investigate its discriminative power as biometric feature. A prototype of a system that extracts the superficial venous pattern of infrared images of back hands will be described. Enhancement algorithms are used to solve the lack of contrast of the infrared images. To trace the veins, a vessel tracking technique is applied, obtaining binary masks of the superficial venous tree. Successively, a method to estimate the blood vessels calibre, length, the location and angles of vessel junctions, will be presented. The discriminative power of these features will be studied, independently and simultaneously, considering two features vector. Pattern matching of two vasculature maps will be performed, to investigate the uniqueness of the vessel network / L’obiettivo del progetto è di sviluppare un metodo automatico per identificare e rappresentare la rete vascolare superficiale presente nel dorso della mano ed investigare sul suo potere discriminativo come caratteristica biometrica. Un prototipo di sistema che estrae l’albero superficiale delle vene da immagini infrarosse del dorso della mano sarà descritto. Algoritmi per il miglioramento del contrasto delle immagini infrarosse saranno applicati. Per tracciare le vene, una tecnica di tracking verrà utilizzata per ottenere una maschera binaria della rete vascolare. Successivamente, un metodo per stimare il calibro e la lunghezza dei vasi sanguigni, la posizione e gli angoli delle giunzioni sarà trattato. Il potere discriminativo delle precedenti caratteristiche verrà studiato ed una tecnica di pattern matching di due modelli vascolari sarà presentata per verificare l’unicità di quest

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

Offline Signature Verification Scheme

Offline signature verification schemes are necessary to determine the authenticity and genuineness of a variety of things which require certification using signatures. Most offline verification schemes till date have required perfect alignment of the signature to the specified axes. However there are situations when the sample to be verified may not be aligned to the required axis. In that situation the current verification schemes could reject the signature even though it may be genuine. The suggested scheme aims to make the verification of signatures size and angle invariant. The invariance can be achieved by scaling and rotational manipulations on the target image. The shape of a person’s signature remains similar in all translational, scaled and rotational alignments of the sign. That is the number of crests, toughs and curves remains the same irrespective of the size and orientation of the image. The ratio between consecutive crests and troughs there by remain the same and hence can be used to determine the genuineness of a signature. The proposed scheme also proposes a novel way to store the information extracted from the image after processing. The ratios obtained for verification can be stored in a linear array, which required much less space as compared to the previously followed schemes. The success of the proposed scheme can be determined from the appreciable FARs and FAAs

Implementation of AES using biometric

Mobile Adhoc network is the most advanced emerging technology in the field of wireless communication. MANETs mainly have the capacity of self-forming, self-healing, enabling peer to peer communication between the nodes, without relying on any centralized network architecture. MANETs are made applicable mainly to military applications, rescue operations and home networking. Practically, MANET could be attacked by several ways using multiple methods. Research on MANET emphasizes on data security issues, as the Adhoc network does not befit security mechanism associated with static networks. This paper focuses mainly on data security techniques incorporated in MANET. Also this paper proposes an implementation of Advanced Encryption Standard using biometric key for MANETs. AES implementation includes, the design of most robust Substitution-Box implementation which defines a nonlinear behavior and mitigates malicious attacks, with an extended security definition. The key for AES is generated using most reliable, robust and precise biometric processing. In this paper, the input message is encrypted by AES powered by secured nonlinear S-box using finger print biometric feature and is decrypted using the reverse process

Fingerprint recognition: A study on image enhancement and minutiae extraction

Fingerprints are a great source for identification of individuals. Fingerprint recognition is one of the oldest forms of biometric identification. However obtaining a good fingerprint image is not always easy. So the fingerprint image must be preprocessed before matching. The objective of this project is to present a better and enhanced fingerprint image. We have studied the factors relating to obtaining high performance feature points detection algorithm, such as image quality, segmentation, image enhancement and feature detection. Commonly used features for improving fingerprint image quality are Fourier spectrum energy, Gabor filter energy and local orientation. Accurate segmentation of fingerprint ridges from noisy background is necessary. For efficient enhancement and feature extraction algorithms, the segmented features must be void of any noise. A preprocessing method consisting of field orientation, ridge frequency estimation, Gabor filtering, segmentation and enhancement is performed. The obtained image is applied to a thinning algorithm and subsequent minutiae extraction. The methodology of image preprocessing and minutiae extraction is discussed. The simulations are performed in the MATLAB environment to evaluate the performance of the implemented algorithms. Results and observations of the fingerprint images are presented at the end

A New Species of Boubou (Malaconotidae: Laniarius) from the Albertine Rift

We describe Laniarius willardi, a new species of boubou shrike (Malaconotidae) from the Albertine Rift of Africa. The most conspicuous, distinguishing morphological feature of the species is a gray to blue-gray iris. This and external morphometric data indicate that L. willardi is diagnosable from other black or sooty boubous. Further, L. willardi is genetically diagnosable, and its closest relative is the Mountain Sooty Boubou (L. poensis camerunensis) from Cameroon. The Crimson-breasted Bush-shrike (L. atrococcineus) and the Lowland Sooty Boubou (L. leucorhynchus) are together the sister clade to L. willardi—L.p. camerunensis. Laniarius willardi and the geographically codistributed L. p. holomelas differ by 11.5% in uncorrected sequence divergence, and elevational data taken from museum specimens suggest the possibility of elevational segregation of the species at ∼2,000 m, withL. willardi occurring at lower elevations. Our broad sampling of black and sooty boubou taxa indicate that (1) races of Mountain Sooty Boubou (L. poensis) do not form a monophyletic clade; (2) L. p. camerunensismay represent multiple, nonsister lineages; and (3) at least one race of Fülleborn\u27s Black Boubou (L. fuelleborni usambaricus) is genetically distinct from other races of that species

Skeleton-based fingerprint minutiae extraction.

by Zhao Feng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 64-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.viTable of Contents --- p.viiList of Figures --- p.ixList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Automatic Personal Identification --- p.1Chapter 1.2 --- Biometrics --- p.2Chapter 1.2.1 --- Objectives --- p.2Chapter 1.2.2 --- Operational Mode --- p.3Chapter 1.2.3 --- Requirements --- p.3Chapter 1.2.4 --- Performance Evaluation --- p.4Chapter 1.2.5 --- Biometric Technologies --- p.4Chapter 1.3 --- Fingerprint --- p.6Chapter 1.3.1 --- Applications --- p.6Chapter 1.3.2 --- Advantages of Fingerprint Identification --- p.7Chapter 1.3.3 --- Permanence and Uniqueness --- p.8Chapter 1.4 --- Thesis Overview --- p.8Chapter 1.5 --- Summary --- p.9Chapter Chapter 2 --- Fingerprint Identification --- p.10Chapter 2.1 --- History of Fingerprints --- p.10Chapter 2.2 --- AFIS Architecture --- p.12Chapter 2.3 --- Fingerprint Acquisition --- p.15Chapter 2.4 --- Fingerprint Representation --- p.16Chapter 2.5 --- Fingerprint Classification --- p.18Chapter 2.6 --- Fingerprint Matching --- p.20Chapter 2.7 --- Challenges --- p.21Chapter 2.8 --- Combination Schemes --- p.22Chapter 2.9 --- Summary --- p.23Chapter Chapter 3 --- Live-Scan Fingerprint Database --- p.24Chapter 3.1 --- Live-Scan Fingerprint Sensors --- p.24Chapter 3.2 --- Database Features --- p.24Chapter 3.3 --- Filename Description --- p.28Chapter Chapter 4 --- Preprocessing for Skeleton-Based Minutiae Extraction --- p.30Chapter 4.1 --- Review of Minutiae-based Methods --- p.31Chapter 4.2 --- Skeleton-based Minutiae Extraction --- p.32Chapter 4.2.1 --- Preprocessing --- p.33Chapter 4.2.2 --- Validation of Bug Pixels and Minutiae Extraction --- p.40Chapter 4.3 --- Experimental Results --- p.42Chapter 4.4 --- Summary --- p.44Chapter Chapter 5 --- Post-Processing --- p.46Chapter 5.1 --- Review of Post-Processing Methods --- p.46Chapter 5.2 --- Post-Processing Algorithms --- p.47Chapter 5.2.1 --- H-Point --- p.47Chapter 5.2.2 --- Termination/Bifurcation Duality --- p.48Chapter 5.2.3 --- Post-Processing Procedure --- p.49Chapter 5.3 --- Experimental Results --- p.52Chapter 5.4 --- Summary --- p.54Chapter Chapter 6 --- Conclusions and Future Work --- p.58Chapter 6.1 --- Conclusions --- p.58Chapter 6.2 --- Problems and Future Works --- p.59Chapter 6.2.1 --- Problem 1 --- p.59Chapter 6.2.2 --- Problem 2 --- p.61Chapter 6.2.3 --- Problem 3 --- p.61Chapter 6.2.4 --- Future Works --- p.62Bibliography --- p.6