STATE OF THE ART IN BIOMETRICS Edited by Jucheng Yang and Loris Nanni State of the Art in Biometrics Edited by Jucheng Yang and Loris Nanni Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Mirna Cvijic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright mashe, 2010. Used under license from Shutterstock.com First published July, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org State of the Art in Biometrics, Edited by Jucheng Yang and Loris Nanni p. cm. ISBN 978-953-307-489-4 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface IX Part 1 Fingerprint Recognition 1 Chapter 1 Fingerprint Quality Analysis and Estimation for Fingerprint Matching 3 Shan Juan Xie, JuCheng Yang, Dong Sun Park, Sook Yoon and Jinwook Shin Chapter 2 Fingerprint Matching using A Hybrid Shape and Orientation Descriptor 25 Joshua Abraham, Paul Kwan and Junbin Gao Chapter 3 Fingerprint Spoof Detection Using Near Infrared Optical Analysis 57 Shoude Chang, Kirill V. Larin, Youxin Mao, Costel Flueraru and Wahab Almuhtadi Chapter 4 Optical Spatial-Frequency Correlation System for Fingerprint Recognition 85 Hiroyuki Yoshimura Chapter 5 On the Introduction of Secondary Fingerprint Classification 105 Ishmael S. Msiza, Jaisheel Mistry, Brain Leke-Betechuoh, Fulufhelo V. Nelwamondo and Tshilidzi Marwala Part 2 Face Recognition 121 Chapter 6 Biologically Inspired Processing for Lighting Robust Face Recognition 123 Ngoc-Son Vu and Alice Caplier Chapter 7 Temporal Synchronization and Normalization of Speech Videos for Face Recognition 143 Usman Saeed and Jean-Luc Dugelay VI Contents Part 3 Iris Recognition 161 Chapter 8 Personal Identity Recognition Approach Based on Iris Pattern 163 Qichuan Tian, Hua Qu, Lanfang Zhang and Ruishan Zong Chapter 9 The State-of-the-Art in Iris Biometric Cryptosystems 179 Christian Rathgeb and Andreas Uhl Chapter 10 Iris Pattern Classification Combining Orientation Recognition 203 Hironobu Takano and Kiyomi Nakamura Part 4 Other Biometrics 219 Chapter 11 Gabor-Based RCM Features for Ear Recognition 221 Ali Pour Yazdanpanah and Karim Faez Chapter 12 Bi-Modality Anxiety Emotion Recognition with PSO-CSVM 235 Ruihu Wang and Bin Fang Chapter 13 Design Approach to Improve Kansei Quality Based on Kansei Engineering 249 Nam-Gyu Kang Part 5 Biometrics Security 265 Chapter 14 Efficiency of Biometric Integration with Salt Value at an Enterprise Level and Data Centres 267 Bhargav Balakrishnan Chapter 15 Chaos-Based Biometrics Template Protection and Secure Authentication 293 Xiaomin Wang, Taihua Xu and Wenfang Zhang Preface Biometric recognition is one of the most widely studied problems in computer science. The use of biometrics techniques, such as face, fingerprints, iris, ears, is a solution for obtaining a secure personal identification. However, the “old” biometrics identification techniques are out of date. The goal of this book is to provide the reader with the most up to date research performed in biometric recognition and to describe some novel methods of biometrics, emphasis on the state of the art skills. The book consists of 15 chapters, each focusing on a most up to date issue. The chapters are divided into five sections- fingerprint recognition, face recognition, iris recognition, other biometrics and biometrics security. Section 1 collects five chapters on fingerprint recognition. Chapter 1 provides an effective fingerprint quality estimation approach in consideration of feature analysis for fingerprint quality estimation. In Chapter 2 the authors propose a novel hybrid shape and orientation descriptor that is designed for fingerprint matching. Chapter 3 gives a combined software-hardware approach to defeat fingerprint spoofing attack, and two methods are presented based on analyzing different optical properties by using optical coherence tomography (OCT) technology and the spectral analysis. In Chapter 4 the authors describe an optical information processing system for biometric authentication using the optical spatial-frequency correlation (OSC) system for the biometric authentication. Chapter 5 demonstrates that the concept of secondary fingerprint classification is feasible and consistent, and uses it to build an additional component into a fingerprint classification. In the section 2 of face recognition, Chapter 6 gives a novel illumination normalization method simulating the performance of retina by combining two adaptive nonlinear functions, a difference of Gaussian filter and a truncation. In Chapter 7 the authors present a novel method of handling the variation caused by lip motion during speech by using temporal synchronization and normalization based on lip motion. Section 3 is a group of iris recognition articles, Chapter 8 presents an iris recognition system based on Local Binary Pattern (LBP) features extraction and selection from multiple images, in which stable features are selected to describe the iris identity while the unreliable feature points are labeled in enrolment template. In Chapter 9 a comprehensive X Preface overview of the state-of-the-art in iris biometric cryptosystems is given. After discussing the fundamentals of iris recognition and biometric cryptosystems, existing key concepts are reviewed and implementations of different variations of iris-based fuzzy commitment are presented. Chapter 10 introduces an iris recognition method using the characteristics of orientation. In the section of other biometrics, Gabor-Based Region covariance matrix (RCM) Features for Ear Recognition is proposed in Chapter 11. In Chapter 12 a fusion method for facial expression and gesture recognition to build a surveillance system by using Particle Swarm Optimization (PSO) and Cascaded SVMs (CSVM) classification is proposed. Chapter 13 examines the role and potential of Kansei and Kansei quality using Kansei engineering case studies, and introduces three case studies to improve Kansei quality in system design. In the last section of biometrics security, Chapter 14 deals with enhancing the efficiency of biometric by integrating it with salt value and encryption algorithms. In Chapter 15 the authors present a novel chaos-based biometrics template protection with secure authentication scheme. The book was reviewed by editors Dr. Jucheng Yang and Dr. Loris Nanni. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Norman Poh, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers. Dr. Jucheng Yang Professor School of Information Technology Jiangxi University of Finance and Economics Nanchang, Jiangxi province China Dr. Loris Nanni Ph.D in Computer Engineering Associate researcher Department of Information Engineering University of Padua Italy [...]... distortions and noises from the skin condition, the pressure, rotation, etc., which can significantly affect the fingerprint alignment and matching process 2.1 Fingerprint acquisition devices The most important part of fingerprint authentication is the fingerprint acquisition devices, which are the components where the fingerprint image is formed The fingerprint quality would influence the matching results since... fingerprints (top row) and low quality fingerprint (bottom row) with different age ranges (Blomeke, et al, 2008) 2.2.3 Age The Biometrics assurance group stated that it is hard to obtain good quality fingerprints from people over the age of 75 due to the lack of definition in the ridges on the pads of the fingers Purdue University has made several inquiries into the image quality of fingerprints and. .. vulgaris and pulpitis sicca (Drahansky, et al, 2010) Fig.5 shows some fingerprint from patients suffering under different skin diseases, either the color of the skin or the ridge lines on the fingertip could be influenced If only the color of the skin is changed, we can avoid the problem by eliminating the optical sensor However, the change of skin structure is very significant; the ridge lines are... extract the fingerprint minutiae features and then compute the quality of each extracted feature to estimate the quality of the fingerprint image into one of five levels The similarity score of a genuine comparison corresponding to the subject, and the similarity score of an impostor comparison between subject and impostor are computed Quality of a biometric sample is then defined as the prediction of a... Part 1 Fingerprint Recognition 1 Fingerprint Quality Analysis and Estimation for Fingerprint Matching Shan Juan Xie1, JuCheng Yang2 ,1, Dong Sun Park1, Sook Yoon3 and Jinwook Shin4 1Department of Electronics and Information Engineering, Chonbuk National University, Jeonju, 2School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 3Dept of Multimedia Engineering, Mokpo... of fingerprint sensing are as follows: (1) Correct readout of fingerprints is impossible in certain cases, such as with shallow grooves (2) When the skin conditions of the finger are unstable; for example, in case of a skin disorder, the finger pattern changes from readout to readout Fingerprint Quality Analysis and Estimation for Fingerprint Matching 5 The principle of the fingerprint acquisition process... due to most of the fingerprint quality estimation methodologies The situation is unfair to the patients; they can not use the fingerprint biometrics system (a) Fingerprints with atopic eczema (b) Fingerprints with psoriasis Fig 5 Fingerprints from patients suffering under different skin diseases For the temporary skin diseases, the users are able to use their fingers for the fingerprint authentication... defined as the distance form a given ridge to adjacent ridges It can be measured as the distance from the centre of one ridge to the centre of another Both the pressure and the humidity of finger will influence the ridge distance The ridge distance of high pressure and wet finger image is narrower than the low pressure and dry finger Since the ridge frequency is the reciprocal of ridge distance and indicates... tests determine the role of temperature and moisture in future fingerprints’ applications Each subject uses six fingers (thumb, index, and middle fingers of both hands) For each finger, the fingerprint impression is given at five levels of air temperature, three levels of pressure and skin humidity The levels of environmental factors and skin conditions used in their experiments are listed in Table 1... Uniformity of the frequency field The uniformity of the frequency field is accomplished by computing the standard deviation of the ridge-to-valley thickness ratio and mapping it into a global score, as large deviation indicates low image quality The frequency field of the image is estimated at discrete points and arranged to a matrix, and the ridge frequency for each point is the inverse of the number of ridges . STATE OF THE ART IN BIOMETRICS Edited by Jucheng Yang and Loris Nanni State of the Art in Biometrics Edited by Jucheng Yang and Loris Nanni Published by InTech. scan the surface of the skin and don’t penetrate the deep skin layer. In case that there are some spots left over or the trace from the previous acquisition of fingerprints, the resulting fingerprint. fingerprint image is formed. The fingerprint quality would influence the matching results since the entire existed matching algorithm has their limitations. The main characteristics of a fingerprint