Atjaunināt sīkdatņu piekrišanu

Reliable Face Recognition Methods: System Design, Implementation and Evaluation 1st ed. Softcover of orig. ed. 2007 [Mīkstie vāki]

  • Formāts: Paperback / softback, 329 pages, height x width: 235x178 mm, weight: 612 g, XIII, 329 p., 1 Paperback / softback
  • Izdošanas datums: 18-Oct-2010
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1441935487
  • ISBN-13: 9781441935489
  • Mīkstie vāki
  • Cena: 91,53 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Standarta cena: 107,69 €
  • Ietaupiet 15%
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
  • Formāts: Paperback / softback, 329 pages, height x width: 235x178 mm, weight: 612 g, XIII, 329 p., 1 Paperback / softback
  • Izdošanas datums: 18-Oct-2010
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1441935487
  • ISBN-13: 9781441935489
One of the challenges for computational intelligence and biometrics is to understand how people process and recognize faces and to develop automated and reliable face recognition systems. Biometrics has become the major component in the complex decision making process associated with security applications. The many challenges addressed for face detection and authentication include cluttered environments, occlusion and disguise, temporal changes, robust training and open set testing.



Reliable Face Recognition Methods seeks to comprehensively address the face recognition problem while gaining new insights from complementary fields of endeavor such as neurosciences, statistics, signal and image processing, computer vision, machine learning and data mining. This book examines the evolution of research surrounding the field to date, explores new directions, and offers specific guidance on the most promising venues for future research and development.



Endorsements by: Ruud Bolle (IBM), John Daugman (Cambridge University, UK), David Zhang (Hong Kong Polytechnic University, China), Stan Li (Chinese Academy of Sciences, China), Tom Huang (University of Illinois, USA).



 
Preface xi
1 Introduction
1(14)
1.1 Tasks and Protocols
4(3)
1.2 Biometrics System Design
7(1)
1.3 History
8(5)
1.4 Road Map
13(2)
2 The Human Face
15(20)
2.1 Cognitive Neurosciences
16(3)
2.2 Psychophysics
19(7)
2.3 The Social Face
26(4)
2.4 Aesthetics and Arts
30(5)
3 Modeling and Prediction
35(18)
3.1 Bayesian Inference
37(3)
3.2 Connectionism
40(3)
3.3 Regularization
43(1)
3.4 Structural Risk Minimization
44(4)
3.5 Transduction
48(2)
3.6 Generative and Discriminative Methods
50(3)
4 Data Collection
53(12)
4.1 Sensing
53(3)
4.2 Standards
56(2)
4.3 Data Compression
58(7)
5 Face Representation
65(32)
5.1 The Face Space
65(3)
5.2 Scale Space
68(4)
5.3 Invariance
72(2)
5.4 Subspace Methods
74(6)
5.5 Feature Selection
80(5)
5.6 Caricatures
85(6)
5.7 Kernel Methods
91(3)
5.8 Color
94(3)
6 Face Recognition
97(24)
6.1 Metrics
97(2)
6.2 Verification and Identification
99(3)
6.3 Open Set Recognition
102(5)
6.4 Watch List and Surveillance
107(3)
6.5 Recognition-by-Parts
110(5)
6.6 Face Selection
115(1)
6.7 Categorization
116(2)
6.8 Morphometrics
118(3)
7 Face in a Crowd
121(34)
7.1 Face Detection
121(4)
7.2 Eye Detection
125(3)
7.3 Uncertainty
128(2)
7.4 Active Learning and Evidence Accumulation
130(5)
7.5 Video Break Detection and Key Frame Extraction
135(2)
7.6 Pose Detection and Manifolds
137(6)
7.7 Tracking and Recognition from Video
143(7)
7.8 Spatio-Temporal Subspace Analysis Using 3D ICA
150(5)
8 3D
155(14)
8.1 Sensing
156(3)
8.2 Analysis by Synthesis
159(1)
8.3 Animation
160(2)
8.4 Modeling and Recognition
162(7)
9 Data Fusion
169(22)
9.1 Multiple Cues
169(2)
9.2 Multiple Engines
171(3)
9.3 Voting Schemes
174(3)
9.4 Multiple Samples
177(2)
9.5 Multimodal Sensory Integration
179(2)
9.6 Soft Biometrics
181(1)
9.7 Boosting and Strangeness
182(9)
10 Denial and Deception
191(22)
10.1 Human Biases
192(1)
10.2 Camouflage
193(2)
10.3 Aliveness Detection
195(1)
10.4 Hallucinations
196(1)
10.5 Asymmetric Faces
197(2)
10.6 Adaptive and Robust Correlation Filters
199(9)
10.7 Associative, Distributed and Holographic Memories
208(5)
11 Augmented Cognition
213(10)
11.1 Paralanguage
214(1)
11.2 Face Expressions
215(3)
11.3 W5+
218(5)
12 Performance Evaluation
223(20)
12.1 Figures of Merit
223(6)
12.2 Score Normalization
229(2)
12.3 Decision Thresholds
231(2)
12.4 Comparative Assessment
233(3)
12.5 Data Bases
236(4)
12.6 Virtual Samples
240(3)
13 Error Analysis
243(18)
13.1 Confidence Intervals
244(4)
13.2 Prevalence and Fallacies
248(3)
13.3 Pattern Specific Error Inhomogeneities
251(4)
13.4 Large Scale Biometric Evaluations
255(6)
14 Security and Privacy
261(14)
14.1 Diversity and Uniqueness
264(2)
14.2 Regeneration of Face Images from Biometric Templates
266(1)
14.3 Cryptography
267(1)
14.4 Steganography and Digital Watermarking
268(2)
14.5 Anonymity
270(3)
14.6 Photofits
273(2)
15 e-Science and Computing
275(8)
16 Epilogue
283(6)
References 289(34)
Index 323