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211002s2021 sz ob 000 0 eng d |
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|a 1272998875
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|a 10.1007/978-3-030-81982-8
|2 doi
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|a (OCoLC)1272957285
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|a HCDD
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|a Advanced methods for human biometrics /
|c Nabil Derbel, Olfa Kanoun, editors.
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|a Cham :
|b Springer,
|c 2021.
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|a 1 online resource
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|a text
|b txt
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|a online resource
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|b PDF
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| 490 |
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|a Smart Sensors, Measurement and Instrumentation ;
|v v. 40
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| 505 |
0 |
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|a Intro -- Preface -- Contents -- Part I Authentication Based on Measurements of Human Characteristics -- 1 Efficient Fingerprint Analysis Based on Sweat Pore Map -- 1.1 Introduction -- 1.2 Related Works -- 1.3 Proposed Approach -- 1.3.1 Step 1: Pores Detection -- 1.3.2 Step 2: Features Extraction -- 1.3.3 Step 3: Pores Alignment -- 1.3.4 Step 4: Pores Matching -- 1.4 Experiments and Performance Evaluation -- 1.4.1 Data Base -- 1.4.2 Training and Test Process -- 1.4.3 Feature Matching -- 1.4.4 Performance Evaluation -- 1.5 Conclusion -- References
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|a 2 Fingerprint Recognition Based on Level Three Features -- 2.1 Introduction -- 2.2 Biometry Background -- 2.2.1 Biometric Systems -- 2.2.2 Biology of the Fingerprint -- 2.3 Pores Detection -- 2.3.1 Related Works -- 2.3.2 Proposed Method -- 2.4 Pores Matching -- 2.4.1 Related Works -- 2.4.2 Proposed Method -- 2.5 Experimental Results -- 2.5.1 Database -- 2.5.2 Pores Detection -- 2.5.3 Recognition -- 2.6 Conclusion -- References -- 3 Fractal Analysis for Iris Multimodal Biometry -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Feature Extraction Based on Fractal Analysis
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|a 3.4 Uni-Modal Recognition System -- 3.4.1 PBMLTiris Database Description -- 3.4.2 Pre-processing -- 3.4.3 Iris Segmentation (Daugman's Operator) -- 3.4.4 Normalization Based on the Pseudo-Polar Method (Masak, ch3AmenispsbibspsMaek2003RecognitionOH) -- 3.4.5 Matching -- 3.5 Multi-modal Recognition System -- 3.5.1 Limitations of Uni-Modal Recognition System (Singh et al., ch3Amenispsbibspssingh2019comprehensive) -- 3.5.2 Fusion Sources -- 3.5.3 Fusion Levels -- 3.6 Experimental Results -- 3.6.1 Segmentation Results -- 3.6.2 Uni-Modal System Evaluation -- 3.6.3 Feature Level Fusion Results
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|a 3.6.4 Sensor Level Fusion Results -- 3.6.5 Score Level Fusion Results -- 3.7 Discussion and Conclusion -- References -- Part II Authentication by Biological Signals -- 4 Security with ECG Biometrics -- 4.1 Biometrics Definition -- 4.2 Biometrics with ECG -- 4.3 ECG Biometrics Approaches -- 4.3.1 Fiducial Approaches -- 4.3.2 Non-fiducial Approaches -- 4.4 ECG Signal Filters -- 4.5 ECG Biometric Classifiers -- 4.6 Evaluation of ECG Biometrics -- 4.7 Conclusion -- References -- 5 ECG Biometric System for Human Recognition Based on the Possibility Theory -- 5.1 Introduction -- 5.2 Possibility Theory
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|a 5.2.1 Possibility Distribution -- 5.2.2 Transformation from Probability Distribution to Possibility Distribution -- 5.3 Methodology -- 5.3.1 ECG Signal Pre-processing -- 5.3.2 Feature Extraction -- 5.3.3 Possibility Theory Based ECG Classification -- 5.3.4 Experimental Results and Discussion -- 5.4 Conclusion -- References -- 6 Surface EMG Based Biometric Person Authentication by a Grasshopper Optimized SVM Algorithm -- 6.1 Introduction -- 6.2 Biometry Based on sEMG Signals -- 6.3 Hybrid Grasshopper Optimization Algorithm and Support Vector Machine (GOA-SVM)
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|a Includes bibliographical references.
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| 520 |
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|a The book highlights recent developments in human biometrics, covering a wide range of methods based on biological signals, image processing, and measurements of human characteristics such as fingerprints and iris or medical characteristics. Human Biometrics is becoming increasingly crucial in forensics security and medicine. They provide a solid basis for identifying individuals based on unique physical characteristics or diseases based on characteristic biomedical measurements. As such, the book offers an essential reference guide about biometry methods for students, engineers, designers, and technicians.
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| 650 |
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|a Biometric identification.
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| 650 |
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7 |
|a Biometric identification
|2 fast
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| 700 |
1 |
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|a Derbel, Nabil.
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| 700 |
1 |
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|a Kanoun, Olfa.
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| 776 |
0 |
8 |
|i Print version:
|t Advanced methods for human biometrics.
|d Cham : Springer, 2021
|z 3030819817
|z 9783030819811
|w (OCoLC)1257890217
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| 830 |
|
0 |
|a Smart sensors, measurement and instrumentation ;
|v 40.
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-81982-8
|y Click for online access
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| 903 |
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|a SPRING-PHYSICS2021
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| 994 |
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|a 92
|b HCD
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