book

Signal and Image Processing for Biometrics

by Amine Naït-Ali, Régis Fournier

August 2012

Intermediate to advanced

336 pages

8h 45m

English

Wiley

Read now

Unlock full access

Cover
Title Page
Copyright
Preface
Chapter 1. Introduction to Biometrics
1.1. Background: from anthropometry to biometrics1.2. Biometrics today1.3. Different modes of use of a biometric system and associated uses1.4. Biometrics as a pattern recognition problem1.4.1. Capture module: from the sensor to the image1.4.2. From the image to the features1.4.3. The matching1.5. Evaluation of different modalities1.6. Quality1.7. Multimodality1.8. Biometrics and preservation of privacy1.9. Conclusion1.10. Bibliography
Chapter 2. Introduction to 2D Face Recognition
2.1. Introduction2.2. Global face recognition techniques2.2.1. Principal component analysis2.2.2. Face recognition using PCA2.2.3. Linear discriminant analysis2.2.4. Face recognition using LDA2.3. Local face recognition techniques2.3.1. Geometric techniques2.3.2. Elastic graph matching techniques2.4. Hybrid face recognition techniques2.5. Some guidances2.6. Some databases2.7. Conclusion2.8. Bibliography
Chapter 3. Facial Soft Biometrics for Person Recognition
3.1. Introduction to soft biometrics3.1.1. Domains of application3.1.2. Related work3.2. Soft biometric systems for human identification3.2.1. Spread of the category set Φ3.2.2. Bounding N for a given interference probability3.2.3. Simulation evaluation of the system in the interference-limited setting of very high sensor resolution3.3. Overall error probability of a soft biometrics system3.3.1. Perr of a soft biometric system in a frontal-to-side re-identification scenario3.4. Conclusions and future directions3.5. Bibliography
Chapter 4. Modeling, Reconstruction and Tracking for Face Recognition
4.1. Background4.1.1. Applications of face recognition4.1.2. On-the-fly authentication4.2. Types of available information4.2.1. Information related to the acquisition system4.2.2. Facial features4.3. Geometric approaches for the reconstruction4.3.1. Stereovision — Multiview4.3.2. Shape from shading4.4. Model-based approaches for reconstruction4.4.1. Modeling of the face4.4.1.1. 2D modeling of the face4.4.1.2. 3D modeling of the face4.4.2. Estimation of the model parameters4.4.2.1. Joint shape and texture estimation4.4.2.2. Shape parameter estimation and texture extraction4.5. Hybrid approaches4.6. Integration of the time aspect4.6.1. Face tracking4.6.2. Static approach from video streams4.6.3. Time consolidation from video streams4.7. Conclusion4.8. Bibliography
Chapter 5. 3D Face Recognition
5.1. Introduction5.2. 3D face databases5.2.1. FRGC5.2.2. GavabDB5.2.3. 3DTEC5.3. 3D acquisition5.4. Preprocessing and normalization5.4.1. Sensor noise processing5.4.2. Processing of holes5.4.3. Localization of anthropometric landmarks5.4.3.1. Curvature analysis5.4.3.2. Symmetry plane localization5.4.3.3 Linear shapes and appearance models5.4.3.4. Multidecision5.4.3.5. Preprocessing and normalization: a case study5.4.3.5.1. Localization of anthropometric landmarks5.5. 3D face recognition5.5.1. 3D face recognition based on local features matching: a case study5.5.1.1. Face representation based on MSeLBP5.5.1.2. Extraction and matching of local features5.5.1.2.1. Spatial constraints5.5.1.2.2. Configuration constraint5.5.1.3. Similarities fusion5.5.1.4. Experimental results5.6. Asymmetric face recognition5.7. Conclusion5.8. Bibliography
Chapter 6. Introduction to Iris Biometrics
6.1. Introduction6.2. Iris biometric systems6.3. Iris recognition methods: state-of-the-art6.4. Preprocessing of iris images6.4.1. Extraction of the region of interest6.4.2. Construction of the noise mask6.4.3. Normalization6.5. Features extraction and encoding6.6. Similarity measure between two IrisCodes6.7. Iris biometrics: emerging methods6.8. Conclusion6.9. Bibliography

Chapter 7. Voice Biometrics: Speaker Verification and Identification
7.1. Introduction7.1.1. Voice biometric techniques7.1.2. Challenge of speaker recognition on mobile devices7.2. Acoustic analysis for robust speaker recognition7.2.1. Mel-frequency analysis7.2.2. Wiener filtering for noise reduction7.3. Distributed speaker recognition through UBM—GMM models7.3.1. Bayesian adaptation to target models7.3.2. Scoring technique for speaker identification7.3.3. Likelihood ratio for speaker verification7.3.4. Normalization of the verification score and Z-norm7.4. Performance evaluation of DSIDV7.4.1. Corpus7.4.2. Experimental protocol7.4.3. Experimental results7.4.3.1. Speaker identification7.4.3.2. Speaker verification7.5. Conclusion7.6. Bibliography
Chapter 8. Introduction to Hand Biometrics
8.1. Introduction8.2. Characterization by minutiae extraction8.2.1. Histogram equalization8.2.2. Binarization8.2.3. Skeletonization (thinning)8.2.4. Detection of minutiae8.2.5. Matching8.2.6. Evaluation of performances8.3. A few databases8.3.1. Fingerprint verification competition (FVC 2000, 2002, 2004, 2006)8.3.2. CASIA fingerprint8.3.3. Wet and wrinkled fingerprint8.3.4. The HK Polytechnic University fingervein image database [HKF]8.3.5. CASIA palmprint (visible/multispectral)8.3.6. Database (THUPALMLAB)8.4. Conclusion8.5. Bibliography
Chapter 9. Multibiometrics
9.1. Introduction9.2. Different principles of multibiometrics9.3. Fusion levels9.3.1. Capture fusion9.3.2. Feature fusion9.3.3. Score fusion9.3.3.1. Fusion based on score transformation9.3.3.1.1. Normalization of scores9.3.3.1.2. Combination of scores using functions9.3.3.2. Fusion using a classifier9.3.3.3. Fusion by using a model of densities9.3.3.4. Improvements9.3.4. Fusion of decision and rank9.3.4.1. Rank fusion9.3.4.2. Decision fusion9.3.5. Evaluation9.4. Applications and illustrations9.5. Conclusion9.6. Bibliography
Chapter 10. Hidden Biometrics
10.1. Introduction10.2. Biometrics using ECG10.3. Biometrics using EMG: preliminary experiments10.4. Biometrics using medical imaging10.4.1. Biometrics using MRI images10.4.2. Biometrics with X-ray images10.5. Conclusion10.6. Bibliography
Chapter 11. Performance Evaluation of Biometric Systems
11.1. Introduction11.2. Reminders on biometric systems11.2.1. Biometrics11.2.2. Biometric characteristics11.2.3. Biometric models11.2.4. Enrollment, verification and identification11.2.5. Architecture of a biometric system11.3. Results analysis tools11.3.1. Performance of biometric systems11.3.1.1. Measures of error rate11.3.1.2. Measures of processing time and memory usage11.3.1.3. Performance curves11.3.1.4. Performance points11.3.1.5. Confidence interval11.3.1.6. Discussion11.3.2. Benchmarks11.3.2.1. Real databases11.3.2.2. Synthetic databases11.4. Illustration of the GREYC-Keystroke system11.4.1. Evaluation protocol11.4.1.1. The GREYC-Keystroke system11.4.1.2. The used database11.4.2. Experimental results11.5. Conclusion11.6. Bibliography
Chapter 12. Classification Techniques for Biometrics
12.1. Introduction12.2. Generalization aptitude and performance measures12.3. Parametric approaches12.3.1. Nave Bayesian classification12.3.2. Linear discriminant analysis12.4. Non-parametric approaches12.4.1. KNN classifier12.4.2. Classification using artificial neural networks12.4.2.1. Structure of an artificial neuron12.4.2.2. Neural network architecture12.4.2.3. Backpropagation algorithm12.4.3. Support vector machine12.4.3.1. The concept of SVMs12.4.3.2. Extension of SVMs to the multiclass case12.4.3.3. SVM in practice12.5. Conclusion12.6. Bibliography
Chapter 13. Data Cryptography
13.1. Introduction13.2. Cryptography13.2.1. Introduction to modern cryptography13.2.2. Definitions13.2.3. Classification of modern crytography13.2.3.1. Symmetric key cryptography13.2.3.1.1. Block ciphers13.2.3.1.2. Stream ciphers13.2.3.1.3. The advanced encryption standard (AES)13.2.3.2. Modes of operation13.2.3.3. Asymmetric cryptography13.2.3.3.1. RSA cryptosystem13.2.3.3.2. Pallier encryption scheme13.2.3.3.3. Homomorphic properties of public key cryptography13.2.4. Cryptanalysis13.3. Conclusion13.4. Bibliography
Chapter 14. Visual Data Protection
14.1. Introduction14.2. Visual data hiding14.2.1. Digital watermarking14.2.1.1. Classification of digital watermarking14.2.1.2. Watermarking embedding techniques14.2.2. Digital fingerprinting14.2.2.1. Classification of digital fingerprinting14.2.2.2. Attacks against fingerprinting codes14.3. A proposed homomorphism-based visual secret sharing scheme14.3.1. Image encryption procedure in the proposed scheme14.3.2. The proposed image sharing scheme14.3.2.1. Scenario14.3.2.2. Overview14.3.2.3. The encryption step14.3.2.4. Decryption and extraction for the Paillier-based scheme14.3.2.5. Decryption and extraction for the RSA-based scheme14.3.3. Experimental results and discussion14.3.3.1. Scheme using the Paillier cryptosystem14.3.3.2. Extraction with the l key-images14.3.3.3. A model of generalized (l – 1, l)14.4. Conclusion14.5. Bibliography
Chapter 15. Biometrics in Forensics
15.1. Introduction15.2. Facial comparison15.2.1. Biometrics dedicated to forensic approximation15.2.2. The problem of facial comparison for forensic assessment.15.3. Voice comparison in forensics15.3.1. Introduction15.3.2. Particularities of the voice modality in the field of biometrics15.3.3. Voice comparison and forensic assessment15.3.4. Inference of identity in forensics15.3.5. Automatic voice comparison15.3.6. Conclusion15.4. Bibliography
List of Authors
Index

Content preview from Signal and Image Processing for Biometrics

Index

face acquisition

face landmark localization

face preprocessing

face recognition

modeling

reconstruction

active acquisition

active shape model

AES

AFNOR

ANN

ANSI

asymmetric encryption

authentication group

backpropagation algorithm

bag of soft biometrics

bag of words

behavioral biometrics

benchmarks

bifurcation

biometric authentication

biosignals

block cipher

body scanner

brain

braincode

bunch

Canny

capacitive sensor

CASIA

CEN

CENELEC

classifiers performances assessment

confidence interval

contactless biometrics

cryptography

cumulative match characteristic curve

data hiding

database

digits

distributed speaker recognition,

ECG

eigenfaces

eigenvalues

eigenvectors

elastic bunch graph matching

elastic graph matching

EMG

enrolment

ETSI

evaluation

expression

face recognition

face tracking

failure-to-acquire

failure-to-enroll rate

false acceptance rate

false match rate

false non-match rate

false rejection rate

false-negative identification-error rate

false-positive identification-error rate

feature extraction

fingerprint

fingerprint sensors

Fisherfaces

fusion

fusion normalisation methods

FVC

G, H

Gabor

Gaussian mixture models

Hamming distance

hidden biometrics

histogram equalisation

Hough transform

human attributes

human identification

hybrid systems

identification rate

iris

iris recognition

at a distance

on the move

iriscode

ISO

K, L

k-nearest-neighbors

keystroke dynamics

learning

light biometrics

linear discriminant analysis (LDA)

Log-Gabor ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Academic Press Library in Signal Processing

Publisher Resources

ISBN: 9781118588192Purchase book

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Signal and Image Processing for Biometrics

by Amine Naït-Ali, Régis Fournier

Index

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.