On the Discrimination Power of Dynamic Features for Online Signature

The mobile market has taken huge leap in the last two decades, re-defining the rules of communication, networking, socializing and transactions among individuals and organizations. Authentication based on verification of signature on mobile devices, is slowly gaining popularity. Most online signature verification algorithms focus on computing the global Equal Error Rate across all users for a dataset. In this work, contrary to such a representation, it is shown that there are user-specific differences on the combined features and user-specific differences on each feature of the Equal Error Rate(EER) values. The experiments to test the hypothesis is carried out on the two publicly available dataset using the dynamic time warping algorithm. From the experiments, it is observed that for the MCYT-100 dataset, which yields an overall EER of 0.08, the range of user-specific EER is between 0 and 0.27

Query by Example of Speaker Audio Signals using Power Spectrum and MFCCs

Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Acquisition and Recognition of 3D Signature

Táto práca sa zaoberá metódami snímania podpisov v 3D priestore, výberom vhodného modelu snímania, získaním dostatočného počtu vzoriek na vytvorenie databázy a nakoniec overovaním podpisov. V prvej časti je spracovaná problematika existujúcich riešení a spôsobov overovania podpisov, ďalej spracovanie obrazu potrebné pre účely snímania markeru v 3D priestore. Nasledujúce časti sú venované návrhu unikátneho riešenia podpisovania sa v priestore perom absenciou akéhokoľvek kontaktu. Boli navrhnuté dva modely snímania a to pomocou kamier alebo senzoru Leap Motion. Aplikácia bola implementovaná nad týmto senzorom a systém overovania dynamických podpisov pomocou algoritmu DTW. Ďalej práca obsahuje popis vytvorenia databázy a experimentálne overenie podpisov. Na konci nájdeme zhodnotenie bezpečnosti a chybovosti systému, ktoré je porovnané s inými metódami. Výsledkom práce je funkčná aplikácia na snímanie a rozpoznávanie 3D podpisov s potenciálom novej bezpečnej techniky podpisovania.This work deals with methods of signing in 3D space, selecting a suitable scan model, obtaining a sufficient number of samples to create a database, and finally verifying signatures. The first part deals with the issue of existing solutions and methods of signature verification, further image processing required for marker shooting in 3D space. The following sections are dedicated to design a unique signature solution in free space using a pen without any contact. Two shooting models have been designed using cameras or Leap Motion sensor. The application was implemented based on DTW algorithm using this sensor resulting in a dynamic signature verification system. Furthermore, the work includes a description using of database creation and experimental signature verification. At the end, we find an assessment of the security and error rate of the system that is compared to other methods. The result of this thesis is an application for 3D signature capture and recognition with the potential of a new technique for secure signature.

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Handwritten signatures are used in authentication systems as a universal biometric identifier. Signature authenticity verification requires building and training a classifier. This paper describes a new approach to the verification of handwritten signatures by dynamic characteristics with a fuzzy rule-based classifier. It is suggested to use the metaheuristic Gravitational Search Algorithm for the selection of the relevant features and tuning fuzzy rule parameters. The efficiency of the approach was tested with an original dataset; the type II errors in finding the signature authenticity did not exceed 0.5% for the worst model and 0.08% for the best model