## Biometric Image Discrimination TechnologiesBiometric Image Discrimination Technologies addresses highly relevant issues to many fundamental concerns of both researchers and practitioners of biometric image discrimination (BID) in biometric applications. This book describes the basic concepts necessary for a good understanding of BID and answers some important introductory questions about BID. Biometric Image Discrimination Technologies covers the theories which are the foundations of basic BID technologies, while developing new algorithms which are verified to be more effective in biometrics authentication. This book will assist students new to the field and will also be useful to senior researchers in this area. This book is a part of the Computational Intelligence and its Applications Series. |

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### Contents

1 | |

5 | |

7 | |

What are BID Technologies? | 8 |

History and Development of BID Technologies | 9 |

AppearanceBased BID Technologies | 12 |

Principal Component Analysis | 21 |

Definitions and Technologies | 22 |

Approach Results | 190 |

Experimental Results | 196 |

Summary | 202 |

Discriminant DCT Feature Extraction | 205 |

Approach Definition and Description | 206 |

Experiments and Analysis | 213 |

Summary | 220 |

Other Typical BID Improvements | 222 |

NonLinear PCA Technologies | 34 |

Summary | 38 |

Linear Discriminant Analysis | 41 |

LDA Definitions | 49 |

NonLinear LDA Technologies | 56 |

Summary | 61 |

PCALDA Applications in Biometrics | 65 |

Face Recognition | 66 |

Palmprint Identification | 80 |

Gait Application | 95 |

Ear Biometrics | 107 |

Speaker Identification | 112 |

Iris Recognition | 117 |

Signature Verification | 123 |

Summary | 130 |

Statistical Uncorrelation Analysis | 139 |

Basic Definition | 140 |

Uncorrelated Optimal Discrimination Vectors UODV | 141 |

Improved UODV Approach | 143 |

Experiments and Analysis | 149 |

Summary | 154 |

Solutions of LDA for Small Sample Size Problems | 156 |

Overview of Existing LDA Regularization Techniques | 158 |

A Unified Framework for LDA | 159 |

A Combined LDA Algorithm for SSS Problem | 164 |

Experiments and Analysis | 171 |

Summary | 184 |

An Improved LDA Approach | 187 |

Definition and Notations | 189 |

Dual Eigenspaces Method | 223 |

PostProcessing on LDABased Method | 225 |

Summary | 232 |

Complete Kernel Fisher Discriminant Analysis | 235 |

Theoretical Perspective of KPCA | 237 |

KPCA Plus LDA | 239 |

Complete KFD Algorithm | 243 |

Experiments | 248 |

Summary | 255 |

2D Image MatrixBased Discriminator | 258 |

2D Image MatrixBased PCA | 259 |

2D Image MatrixBased LDA | 274 |

Summary | 284 |

TwoDirectional PCALDA | 287 |

Basic Models and Definitions | 290 |

TwoDirectional PCA Plus LDA | 304 |

Experimental Results | 307 |

Summary | 324 |

Feature Fusion Using Complex Discriminator | 329 |

Serial and Parallel Feature Fusion Strategies | 331 |

Complex Linear Projection Analysis | 332 |

Feature Preprocessing Techniques | 335 |

Symmetry Property of Parallel Feature Fusion | 337 |

Biometric Applications | 339 |

Summary | 348 |

About the Authors | 351 |

353 | |

### Other editions - View all

Biometric Image Discrimination Technologies David Zhang,Xiaoyuan Jing,Jian Yang Limited preview - 2006 |

Biometric Image Discrimination Technologies David Zhang,Xiaoyuan Jing,Jian Yang No preview available - 2006 |

### Common terms and phrases

2DPCA algorithm Analysis and Machine approach BDPCA BDPCA+LDA Belhumeur between-class scatter BID technologies biometrics chapter CKFD classifier Computer Vision Copying or distributing Copyright corresponding covariance matrix dimension dimensionality discriminant analysis discriminant features discriminatory information distance distributing in print DLDA eigenface eigenspace eigenvalues eigenvectors electronic forms Equation face database face images face recognition feature extraction feature fusion feature space feature vector Fisher criterion Fisher discriminant fisherface method forms without written Hespanha high-dimensional Idea Group Inc IEEE Transactions kernel fisherface KPCA Kriegman LDA algorithm linear discriminant Machine Intelligence null space number of training obtained optimal discriminant vectors ORL database palmprint database palmprint images Pattern Analysis Pattern Recognition PCA plus LDA PCA+LDA post-processing principal components print or electronic recognition rates scatter matrix Schölkopf sub-images subspace technique training samples training set Transactions on Pattern uncorrelated UODV Wang within-class scatter matrix written Figure Zhang

### Popular passages

Page vii - knowledge" of the authorized person. This is why biometrics identification or verification system started to be more focused in the recent years.

Page 2 - The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.