| Vorliegende Sprache |
eng |
| Hinweise auf parallele Ausgaben |
366166093 Buchausg. u.d.T.: ‡Liu, Chengjun: Cross disciplinary biometric systems |
| ISBN |
978-3-642-28456-4 |
| Name |
Liu, Chengjun |
| Mago, Vijay Kumar |
| Name ANZEIGE DER KETTE |
Mago, Vijay Kumar |
| T I T E L |
Cross Disciplinary Biometric Systems |
| Verlagsort |
Berlin, Heidelberg |
| Verlag |
Springer Berlin Heidelberg |
| Erscheinungsjahr |
2012 |
| 2012 |
| Umfang |
Online-Ressource (XVI, 228p. 112 illus., 58 illus. in color, digital) |
| Reihe |
Intelligent Systems Reference Library ; 37 |
| Notiz / Fußnoten |
Description based upon print version of record |
| Weiterer Inhalt |
Title; Preface; Acknowledgements; Contents; List of Contributors; Acronyms; Feature Local Binary Patterns; Introduction; Feature Local Binary Patterns; Feature Local Binary Patterns Form 1; Feature Local Binary Patterns Form 2; LBP with Relative Bias Thresholding for Feature Pixel Extraction; Conclusion; References; New Color Features for Pattern Recognition; Introduction; Innovative Color Features from the Primary and the Subtraction of the Primary Colors; A New Pattern Recognition Framework Using the Effective Color Features. A New Similarity Measure for the Proposed Pattern Recognition FrameworkExperiments; Conclusions; References; Gabor-DCT Features with Application to Face Recognition; Introduction; Background; Discriminative Color Facial Parts; Multiple Scale and Multiple Orientation Gabor Image Representation; The Gabor-DCT Features (GDF) Method; Experiments; Data Sets; Experiments on FRGC; Experiments on Multi-PIE; Conclusions; References; Frequency and Color Fusion for Face Verification; Introduction; The Hybrid Color Space: RIQ; Multiple Frequency Feature Fusion for Face Representation. Feature Extraction Using an Improved Fisher ModelExperiments; FRGC Database; Effectiveness of the Hybrid Color Space; Multiple Frequency Feature Fusion for Face Verification; Multiple Spatial Feature Fusion for Face Verification; Illumination Normalization for Face Verification; Conclusion; References; Mixture of Classifiers for Face Recognition across Pose; Introduction; Background; Pose Classification; Face Recognition in a Pose Class; Face Recognition across Pose; Experiments of Face Recognition across Pose; The CMU PIE Database and Data Preparation. Experimental Results of Pose ClassificationExperimental Results of Face Recognition in a Pose Class; Experimental Results of Face Recognition across Pose; Conclusions; References; Wavelet Features for 3D Face Recognition; Introduction; Convolution Filters; Gaussian Derivative Filter; Morlet Filter; Complex Morlet Filter; Complex Frequency B-Spline Filter; Face Recognition Algorithms; FRGC and the BEE Baseline Algorithm; Convolution Features and Decision Fusion; Experiments; Conclusion; References; Minutiae-Based Fingerprint Matching; Introduction; Minutiae-Based Techniques. Problem FormulationSimilarity Score; Local Minutiae Matching; Minutia Cylinder-Code; The Local Descriptors; Global Score and Consolidation; Performance Evaluation of Local Minutia Descriptors; Performance Evaluation of Recent MCC Improvements; Conclusions; References; Iris Segmentation: State of the Art and Innovative Methods; Introduction; Segmentation of the Iris Pattern; Methods That Approximate the Iris Boundaries by Two Circumferences; Methods Based on A-Priori Models; Methods Based on the Analysis of Local Characteristics; Approaches Based on Active Contours. Hybrid and Incremental Methods |
| Titelhinweis |
Buchausg. u.d.T.: ‡Liu, Chengjun: Cross disciplinary biometric systems |
| ISBN |
ISBN 978-3-642-28457-1 |
| Klassifikation |
UYQ |
| COM004000 |
| 006.3 |
| 006.42 |
| Q342 |
| Kurzbeschreibung |
Cross disciplinary biometric systems help boost the performance of the conventional systems. Not only is the recognition accuracy significantly improved, but also the robustness of the systems is greatly enhanced in the challenging environments, such as varying illumination conditions. By leveraging the cross disciplinary technologies, face recognition systems, fingerprint recognition systems, iris recognition systems, as well as image search systems all benefit in terms of recognition performance. Take face recognition for an example, which is not only the most natural way human beings recognize the identity of each other, but also the least privacy-intrusive means because people show their face publicly every day. Face recognition systems display superb performance when they capitalize on the innovative ideas across color science, mathematics, and computer science (e.g., pattern recognition, machine learning, and image processing). The novel ideas lead to the development of new color models and effective color features in color science; innovative features from wavelets and statistics, and new kernel methods and novel kernel models in mathematics; new discriminant analysis frameworks, novel similarity measures, and new image analysis methods, such as fusing multiple image features from frequency domain, spatial domain, and color domain in computer science; as well as system design, new strategies for system integration, and different fusion strategies, such as the feature level fusion, decision level fusion, and new fusion strategies with novel similarity measures. |
| 1. Schlagwortkette |
Biometrie |
| Bilderkennung |
| Gesicht |
| Auge |
| Daktylogramm |
| Farbbildverarbeitung |
| Maschinelles Lernen |
| Mathematische Methode |
| Aufsatzsammlung |
| ANZEIGE DER KETTE |
Biometrie -- Bilderkennung -- Gesicht -- Auge -- Daktylogramm -- Farbbildverarbeitung -- Maschinelles Lernen -- Mathematische Methode -- Aufsatzsammlung |
| SWB-Titel-Idn |
365271128 |
| Signatur |
Springer E-Book |
| Bemerkungen |
Elektronischer Volltext - Campuslizenz |
| Elektronische Adresse |
$uhttp://dx.doi.org/10.1007/978-3-642-28457-1 |
| Internetseite / Link |
Volltext |
| Siehe auch |
Volltext |
| Siehe auch |
Cover |
