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Signal and Image Processing for Biometrics

Book Description

The aim of this book is to deal with biometrics in terms of signal and image processing methods and algorithms. This will help engineers and students working in digital signal and image processing deal with the implementation of such specific algorithms.

It discusses numerous signal and image processing techniques that are very often used in biometric applications. In particular, algorithms related to hand feature extraction, speech recognition, 2D/3D face biometrics, video surveillance and other interesting approaches are presented. Moreover, in some chapters, Matlab codes are provided so that readers can easily reproduce some basic simulation results.

This book is suitable for final-year undergraduate students, postgraduate students, engineers and researchers in the field of computer engineering and applied digital signal and image processing.

1. Introduction to Biometrics, Bernadette Dorizzi.

2. Introduction to 2D Face Recognition, Amine Nait-Ali and Dalila Cherifi.

3. Facial Soft Biometrics for Person Recognition, Antitza Dantcheva, Christelle Yemdji, Petros Elia and Jean-Luc Dugelay.

4. Modeling, Reconstruction and Tracking 
for Face Recognition, Catherine Herold, Vincent Despiegel, Stéphane Gentric,
Séverine Dubuisson and Isabelle Bloch.

5. 3D Face Recognition, Mohsen Ardabilian, Przemyslaw Szeptycki, Di Huang and Liming Chen.

6. Introduction to Iris Biometrics, Kamel Aloui, Amine Nait-Ali, Régis Fournier and Saber Naceur.

7. Voice Biometrics: Speaker Verification and Identification, Foezur Chowdhury, Sid-Ahmed Selouani
and Douglas O'Shaughnessy.

8. Introduction to Hand Biometrics, Régis Fournier and Amine Nait-Ali.

9. Multibiometrics, Romain Giot, Baptiste Hemery, Estelle Cherrier and
Christophe Rosenberger.

10. Hidden Biometrics, Amine Nait-Ali, Régis Fournier, Kamel Aloui and
Noureddine Belgacem.

11. Performance Evaluation of Biometric Systems, Mohamad El-Abed, Romain Giot, Baptiste Hemery, Julien Mahier
and Christophe Rosenberger.

12. Classification Techniques for Biometrics, Amel Bouchemha, Chérif Nait-Hamoud, Amine Nait-Ali and
Régis Fournier.

13. Data Cryptography, Islam Naveed and William Puech.

14. Visual Data Protection, Islam Naveed and William Puech.

15. Biometrics in Forensics, Guillaume Galou and Christophe Lambert.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. Chapter 1. Introduction to Biometrics
    1. 1.1. Background: from anthropometry to biometrics
    2. 1.2. Biometrics today
    3. 1.3. Different modes of use of a biometric system and associated uses
    4. 1.4. Biometrics as a pattern recognition problem
      1. 1.4.1. Capture module: from the sensor to the image
      2. 1.4.2. From the image to the features
      3. 1.4.3. The matching
    5. 1.5. Evaluation of different modalities
    6. 1.6. Quality
    7. 1.7. Multimodality
    8. 1.8. Biometrics and preservation of privacy
    9. 1.9. Conclusion
    10. 1.10. Bibliography
  6. Chapter 2. Introduction to 2D Face Recognition
    1. 2.1. Introduction
    2. 2.2. Global face recognition techniques
      1. 2.2.1. Principal component analysis
      2. 2.2.2. Face recognition using PCA
      3. 2.2.3. Linear discriminant analysis
      4. 2.2.4. Face recognition using LDA
    3. 2.3. Local face recognition techniques
      1. 2.3.1. Geometric techniques
      2. 2.3.2. Elastic graph matching techniques
    4. 2.4. Hybrid face recognition techniques
    5. 2.5. Some guidances
    6. 2.6. Some databases
    7. 2.7. Conclusion
    8. 2.8. Bibliography
  7. Chapter 3. Facial Soft Biometrics for Person Recognition
    1. 3.1. Introduction to soft biometrics
      1. 3.1.1. Domains of application
      2. 3.1.2. Related work
    2. 3.2. Soft biometric systems for human identification
      1. 3.2.1. Spread of the category set Φ
      2. 3.2.2. Bounding N for a given interference probability
      3. 3.2.3. Simulation evaluation of the system in the interference-limited setting of very high sensor resolution
    3. 3.3. Overall error probability of a soft biometrics system
      1. 3.3.1. Perr of a soft biometric system in a frontal-to-side re-identification scenario
    4. 3.4. Conclusions and future directions
    5. 3.5. Bibliography
  8. Chapter 4. Modeling, Reconstruction and Tracking for Face Recognition
    1. 4.1. Background
      1. 4.1.1. Applications of face recognition
      2. 4.1.2. On-the-fly authentication
    2. 4.2. Types of available information
      1. 4.2.1. Information related to the acquisition system
      2. 4.2.2. Facial features
    3. 4.3. Geometric approaches for the reconstruction
      1. 4.3.1. Stereovision — Multiview
      2. 4.3.2. Shape from shading
    4. 4.4. Model-based approaches for reconstruction
      1. 4.4.1. Modeling of the face
        1. 4.4.1.1. 2D modeling of the face
        2. 4.4.1.2. 3D modeling of the face
      2. 4.4.2. Estimation of the model parameters
        1. 4.4.2.1. Joint shape and texture estimation
        2. 4.4.2.2. Shape parameter estimation and texture extraction
    5. 4.5. Hybrid approaches
    6. 4.6. Integration of the time aspect
      1. 4.6.1. Face tracking
      2. 4.6.2. Static approach from video streams
      3. 4.6.3. Time consolidation from video streams
    7. 4.7. Conclusion
    8. 4.8. Bibliography
  9. Chapter 5. 3D Face Recognition
    1. 5.1. Introduction
    2. 5.2. 3D face databases
      1. 5.2.1. FRGC
      2. 5.2.2. GavabDB
      3. 5.2.3. 3DTEC
    3. 5.3. 3D acquisition
    4. 5.4. Preprocessing and normalization
      1. 5.4.1. Sensor noise processing
      2. 5.4.2. Processing of holes
      3. 5.4.3. Localization of anthropometric landmarks
        1. 5.4.3.1. Curvature analysis
        2. 5.4.3.2. Symmetry plane localization
        3. 5.4.3.3 Linear shapes and appearance models
        4. 5.4.3.4. Multidecision
        5. 5.4.3.5. Preprocessing and normalization: a case study
          1. 5.4.3.5.1. Localization of anthropometric landmarks
    5. 5.5. 3D face recognition
      1. 5.5.1. 3D face recognition based on local features matching: a case study
        1. 5.5.1.1. Face representation based on MSeLBP
        2. 5.5.1.2. Extraction and matching of local features
          1. 5.5.1.2.1. Spatial constraints
          2. 5.5.1.2.2. Configuration constraint
        3. 5.5.1.3. Similarities fusion
        4. 5.5.1.4. Experimental results
    6. 5.6. Asymmetric face recognition
    7. 5.7. Conclusion
    8. 5.8. Bibliography
  10. Chapter 6. Introduction to Iris Biometrics
    1. 6.1. Introduction
    2. 6.2. Iris biometric systems
    3. 6.3. Iris recognition methods: state-of-the-art
    4. 6.4. Preprocessing of iris images
      1. 6.4.1. Extraction of the region of interest
      2. 6.4.2. Construction of the noise mask
      3. 6.4.3. Normalization
    5. 6.5. Features extraction and encoding
    6. 6.6. Similarity measure between two IrisCodes
    7. 6.7. Iris biometrics: emerging methods
    8. 6.8. Conclusion
    9. 6.9. Bibliography
  11. Chapter 7. Voice Biometrics: Speaker Verification and Identification
    1. 7.1. Introduction
      1. 7.1.1. Voice biometric techniques
      2. 7.1.2. Challenge of speaker recognition on mobile devices
    2. 7.2. Acoustic analysis for robust speaker recognition
      1. 7.2.1. Mel-frequency analysis
      2. 7.2.2. Wiener filtering for noise reduction
    3. 7.3. Distributed speaker recognition through UBM—GMM models
      1. 7.3.1. Bayesian adaptation to target models
      2. 7.3.2. Scoring technique for speaker identification
      3. 7.3.3. Likelihood ratio for speaker verification
      4. 7.3.4. Normalization of the verification score and Z-norm
    4. 7.4. Performance evaluation of DSIDV
      1. 7.4.1. Corpus
      2. 7.4.2. Experimental protocol
      3. 7.4.3. Experimental results
        1. 7.4.3.1. Speaker identification
        2. 7.4.3.2. Speaker verification
    5. 7.5. Conclusion
    6. 7.6. Bibliography
  12. Chapter 8. Introduction to Hand Biometrics
    1. 8.1. Introduction
    2. 8.2. Characterization by minutiae extraction
      1. 8.2.1. Histogram equalization
      2. 8.2.2. Binarization
      3. 8.2.3. Skeletonization (thinning)
      4. 8.2.4. Detection of minutiae
      5. 8.2.5. Matching
      6. 8.2.6. Evaluation of performances
    3. 8.3. A few databases
      1. 8.3.1. Fingerprint verification competition (FVC 2000, 2002, 2004, 2006)
      2. 8.3.2. CASIA fingerprint
      3. 8.3.3. Wet and wrinkled fingerprint
      4. 8.3.4. The HK Polytechnic University fingervein image database [HKF]
      5. 8.3.5. CASIA palmprint (visible/multispectral)
      6. 8.3.6. Database (THUPALMLAB)
    4. 8.4. Conclusion
    5. 8.5. Bibliography
  13. Chapter 9. Multibiometrics
    1. 9.1. Introduction
    2. 9.2. Different principles of multibiometrics
    3. 9.3. Fusion levels
      1. 9.3.1. Capture fusion
      2. 9.3.2. Feature fusion
      3. 9.3.3. Score fusion
        1. 9.3.3.1. Fusion based on score transformation
          1. 9.3.3.1.1. Normalization of scores
          2. 9.3.3.1.2. Combination of scores using functions
        2. 9.3.3.2. Fusion using a classifier
        3. 9.3.3.3. Fusion by using a model of densities
        4. 9.3.3.4. Improvements
      4. 9.3.4. Fusion of decision and rank
        1. 9.3.4.1. Rank fusion
        2. 9.3.4.2. Decision fusion
      5. 9.3.5. Evaluation
    4. 9.4. Applications and illustrations
    5. 9.5. Conclusion
    6. 9.6. Bibliography
  14. Chapter 10. Hidden Biometrics
    1. 10.1. Introduction
    2. 10.2. Biometrics using ECG
    3. 10.3. Biometrics using EMG: preliminary experiments
    4. 10.4. Biometrics using medical imaging
      1. 10.4.1. Biometrics using MRI images
      2. 10.4.2. Biometrics with X-ray images
    5. 10.5. Conclusion
    6. 10.6. Bibliography
  15. Chapter 11. Performance Evaluation of Biometric Systems
    1. 11.1. Introduction
    2. 11.2. Reminders on biometric systems
      1. 11.2.1. Biometrics
      2. 11.2.2. Biometric characteristics
      3. 11.2.3. Biometric models
      4. 11.2.4. Enrollment, verification and identification
      5. 11.2.5. Architecture of a biometric system
    3. 11.3. Results analysis tools
      1. 11.3.1. Performance of biometric systems
        1. 11.3.1.1. Measures of error rate
        2. 11.3.1.2. Measures of processing time and memory usage
        3. 11.3.1.3. Performance curves
        4. 11.3.1.4. Performance points
        5. 11.3.1.5. Confidence interval
        6. 11.3.1.6. Discussion
      2. 11.3.2. Benchmarks
        1. 11.3.2.1. Real databases
        2. 11.3.2.2. Synthetic databases
    4. 11.4. Illustration of the GREYC-Keystroke system
      1. 11.4.1. Evaluation protocol
        1. 11.4.1.1. The GREYC-Keystroke system
        2. 11.4.1.2. The used database
      2. 11.4.2. Experimental results
    5. 11.5. Conclusion
    6. 11.6. Bibliography
  16. Chapter 12. Classification Techniques for Biometrics
    1. 12.1. Introduction
    2. 12.2. Generalization aptitude and performance measures
    3. 12.3. Parametric approaches
      1. 12.3.1. Nave Bayesian classification
      2. 12.3.2. Linear discriminant analysis
    4. 12.4. Non-parametric approaches
      1. 12.4.1. KNN classifier
      2. 12.4.2. Classification using artificial neural networks
        1. 12.4.2.1. Structure of an artificial neuron
        2. 12.4.2.2. Neural network architecture
        3. 12.4.2.3. Backpropagation algorithm
      3. 12.4.3. Support vector machine
        1. 12.4.3.1. The concept of SVMs
        2. 12.4.3.2. Extension of SVMs to the multiclass case
        3. 12.4.3.3. SVM in practice
    5. 12.5. Conclusion
    6. 12.6. Bibliography
  17. Chapter 13. Data Cryptography
    1. 13.1. Introduction
    2. 13.2. Cryptography
      1. 13.2.1. Introduction to modern cryptography
      2. 13.2.2. Definitions
      3. 13.2.3. Classification of modern crytography
        1. 13.2.3.1. Symmetric key cryptography
          1. 13.2.3.1.1. Block ciphers
          2. 13.2.3.1.2. Stream ciphers
          3. 13.2.3.1.3. The advanced encryption standard (AES)
        2. 13.2.3.2. Modes of operation
        3. 13.2.3.3. Asymmetric cryptography
          1. 13.2.3.3.1. RSA cryptosystem
          2. 13.2.3.3.2. Pallier encryption scheme
          3. 13.2.3.3.3. Homomorphic properties of public key cryptography
      4. 13.2.4. Cryptanalysis
    3. 13.3. Conclusion
    4. 13.4. Bibliography
  18. Chapter 14. Visual Data Protection
    1. 14.1. Introduction
    2. 14.2. Visual data hiding
      1. 14.2.1. Digital watermarking
        1. 14.2.1.1. Classification of digital watermarking
        2. 14.2.1.2. Watermarking embedding techniques
      2. 14.2.2. Digital fingerprinting
        1. 14.2.2.1. Classification of digital fingerprinting
        2. 14.2.2.2. Attacks against fingerprinting codes
    3. 14.3. A proposed homomorphism-based visual secret sharing scheme
      1. 14.3.1. Image encryption procedure in the proposed scheme
      2. 14.3.2. The proposed image sharing scheme
        1. 14.3.2.1. Scenario
        2. 14.3.2.2. Overview
        3. 14.3.2.3. The encryption step
        4. 14.3.2.4. Decryption and extraction for the Paillier-based scheme
        5. 14.3.2.5. Decryption and extraction for the RSA-based scheme
      3. 14.3.3. Experimental results and discussion
        1. 14.3.3.1. Scheme using the Paillier cryptosystem
        2. 14.3.3.2. Extraction with the l key-images
        3. 14.3.3.3. A model of generalized (l – 1, l)
    4. 14.4. Conclusion
    5. 14.5. Bibliography
  19. Chapter 15. Biometrics in Forensics
    1. 15.1. Introduction
    2. 15.2. Facial comparison
      1. 15.2.1. Biometrics dedicated to forensic approximation
      2. 15.2.2. The problem of facial comparison for forensic assessment.
    3. 15.3. Voice comparison in forensics
      1. 15.3.1. Introduction
      2. 15.3.2. Particularities of the voice modality in the field of biometrics
      3. 15.3.3. Voice comparison and forensic assessment
      4. 15.3.4. Inference of identity in forensics
      5. 15.3.5. Automatic voice comparison
      6. 15.3.6. Conclusion
    4. 15.4. Bibliography
  20. List of Authors
  21. Index