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Preface; Contents; 1 Fingerprint Quality Assessment: Matching Performance and Image Quality; 1.1 Introduction; 1.2 Background; 1.3 Trial Measures; 1.3.1 Metrics with Single Feature; 1.3.2 Segmentation-Based Metrics; 1.3.2.1 FQA via Informative Region; 1.3.2.2 FQA via Pixel-Pruning; 1.3.3 FQA via Multi-feature; 1.4 Experimental Results; 1.4.1 Software; 1.4.2 Database et Protocol; 1.4.3 Results; 1.4.3.1 ES with Quality; 1.4.3.2 Isometric Bins; 1.4.4 Discussion via Sample Utility; 1.5 Conclusion; References.
2 A Novel Perspective on Hand Vein Patterns for Biometric Recognition: Problems, Challenges, and Implementations2.1 Introduction; 2.2 Vein Pattern Scanning Using Optical Methods; 2.2.1 Vein Pattern Visualization; 2.2.2 Structure of a Hand Vein Recognition Device; 2.3 Problems and Challenges in Vein Pattern Applications; 2.4 Modern Perspectives on Vein Structure Recognition; 2.4.1 Ergonomics and Hand Pose Assessment in Vein pattern Identification; 2.4.2 Synthetic Vein Pattern Generation; 2.4.3 Vein Biometrics in a Connected World; 2.5 Conclusions; References.
3 Improving Biometric Identification Performance Using PCANet Deep Learning and Multispectral Palmprint3.1 Introduction; 3.2 Image Features; 3.3 Proposed Methodology; 3.3.1 Feature Extraction; 3.3.1.1 PCANet Deep Learning; 3.3.1.2 Flexibility Property; 3.3.2 Classification; 3.4 Experimental Results and Discussion; 3.4.1 Experimental Databases; 3.4.2 Identification Test Results; 3.4.2.1 Performance of the Unimodal Systems; 3.4.2.2 Performance of the Multimodal Systems; 3.4.3 Comparison Study; 3.5 Conclusion and Further Work; References; 4 Biometric Acoustic Ear Recognition; 4.1 Introduction.
4.2 Ear Biometrics and Acoustics4.2.1 The Ear as a Biometric; 4.2.2 Image Based Ear Recognition; 4.2.3 Acoustic Based Ear Recognition; 4.2.4 Acoustic Properties of The Ear; 4.2.5 Ears Coupled to Headphones; 4.3 Measuring Device; 4.4 Experiments; 4.4.1 Design Considerations; 4.4.2 Data Collection; 4.5 Data Analysis; 4.5.1 Preprocessing; 4.5.2 Initial Data Analysis; 4.5.3 Statistical Attributes; 4.5.4 Feature Selection and Extraction; 4.5.4.1 All Frequency Components; 4.5.4.2 Octave Bands; 4.5.4.3 Acoustic Properties of The Outer Ear; 4.6 Results; 4.6.1 Performance of All Frequency Components.
4.6.2 Performance of PCA4.6.3 Performance of Octave Bands; 4.6.4 Performance of Ear Characteristic Bands and Peaks; 4.7 Discussion and Future Work; 4.8 Conclusion; References; 5 Eye Blinking EOG Signals as Biometrics; 5.1 Introduction; 5.2 Origin of Eye Blinking EOG Signals; 5.3 Proposed Approach for Eye Blinking EOG Biometric System; 5.3.1 Data Acquisition; 5.3.2 Pre-processing; 5.3.3 Feature Extraction; 5.3.4 Feature Selection; 5.3.5 Classification; 5.3.5.1 Linear Decision Rule; 5.3.5.2 Mahalanobis Decision Rule; 5.4 Experimental Setup and Results; 5.4.1 Identification Mode.
2 A Novel Perspective on Hand Vein Patterns for Biometric Recognition: Problems, Challenges, and Implementations2.1 Introduction; 2.2 Vein Pattern Scanning Using Optical Methods; 2.2.1 Vein Pattern Visualization; 2.2.2 Structure of a Hand Vein Recognition Device; 2.3 Problems and Challenges in Vein Pattern Applications; 2.4 Modern Perspectives on Vein Structure Recognition; 2.4.1 Ergonomics and Hand Pose Assessment in Vein pattern Identification; 2.4.2 Synthetic Vein Pattern Generation; 2.4.3 Vein Biometrics in a Connected World; 2.5 Conclusions; References.
3 Improving Biometric Identification Performance Using PCANet Deep Learning and Multispectral Palmprint3.1 Introduction; 3.2 Image Features; 3.3 Proposed Methodology; 3.3.1 Feature Extraction; 3.3.1.1 PCANet Deep Learning; 3.3.1.2 Flexibility Property; 3.3.2 Classification; 3.4 Experimental Results and Discussion; 3.4.1 Experimental Databases; 3.4.2 Identification Test Results; 3.4.2.1 Performance of the Unimodal Systems; 3.4.2.2 Performance of the Multimodal Systems; 3.4.3 Comparison Study; 3.5 Conclusion and Further Work; References; 4 Biometric Acoustic Ear Recognition; 4.1 Introduction.
4.2 Ear Biometrics and Acoustics4.2.1 The Ear as a Biometric; 4.2.2 Image Based Ear Recognition; 4.2.3 Acoustic Based Ear Recognition; 4.2.4 Acoustic Properties of The Ear; 4.2.5 Ears Coupled to Headphones; 4.3 Measuring Device; 4.4 Experiments; 4.4.1 Design Considerations; 4.4.2 Data Collection; 4.5 Data Analysis; 4.5.1 Preprocessing; 4.5.2 Initial Data Analysis; 4.5.3 Statistical Attributes; 4.5.4 Feature Selection and Extraction; 4.5.4.1 All Frequency Components; 4.5.4.2 Octave Bands; 4.5.4.3 Acoustic Properties of The Outer Ear; 4.6 Results; 4.6.1 Performance of All Frequency Components.
4.6.2 Performance of PCA4.6.3 Performance of Octave Bands; 4.6.4 Performance of Ear Characteristic Bands and Peaks; 4.7 Discussion and Future Work; 4.8 Conclusion; References; 5 Eye Blinking EOG Signals as Biometrics; 5.1 Introduction; 5.2 Origin of Eye Blinking EOG Signals; 5.3 Proposed Approach for Eye Blinking EOG Biometric System; 5.3.1 Data Acquisition; 5.3.2 Pre-processing; 5.3.3 Feature Extraction; 5.3.4 Feature Selection; 5.3.5 Classification; 5.3.5.1 Linear Decision Rule; 5.3.5.2 Mahalanobis Decision Rule; 5.4 Experimental Setup and Results; 5.4.1 Identification Mode.