王申培所著的《模式识别、机器智能与生物特征识别(英文版)》收集了世界一流的模式识别、人工智能和生物特征识别技术领域专家编写的31章内容，涵盖模式识别与机器智能、计算机视觉与图像处理、人脸识别与取证、生物特征身份验证等多方面结合的研究。其应用跨越多个领域，从工程、科学研究和实验，到生物医学和医学诊断，再到身份认证和国土安全。本书内容系统、全面、新颖，理论与典型应用实例相结合，主要服务于信息安全、模式识别、计算机应用、人工智能等领域专业人员。

王申培所著的《模式识别、机器智能与生物特征识别(英文版)》介绍广泛应用的人工智能技术——模式识别及其应用的最新进展，收集了世界一流的模式识别、人工智能和生物特征识别技术领域专家编写的31章内容，涵盖模式识别与机器智能、计算机视觉与图像处理、人脸识别与取证、生物特征身份验证等多方面结合的研究。其应用跨越多个领域，从工程、科学研究和实验，到生物医学和医学诊断，再到身份认证和国土安全。此外，《模式识别、机器智能与生物特征识别(英文版)》还介绍了人类行为的计算机建模和仿真。

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Part Ⅰ: Pattern Recognition and Machine Intelligence

 1 A Review of Applications of Evolutionary Algorithms in Pattern Recognition

 1.1 Introduction

 1.2 Basic Notions of Evolutionary Algorithms

 1.3 A Review of EAs in Pattern Recognition

 1.4 Future Research Directions

 1.5 Conclusions

 References

 2 Pattern Discovery and Recognition in Sequences

 2.1 Introduction

 2.2 Sequence Patterns and Pattern Discovery-A Brief Review.

 2.3 Our Pattern Discovery Framework

 2.4 Conclusion

 References

 3 A Hybrid Method of Tone Assessment for Mandarin

 CALL System

 3.1 Introduction

 3.2 Related Work

 3.3 Proposed Approach

 3.4 Experimental Procedure and Analysis

 3.5 Conclusions

 References

 4 Fusion with Infrared Images for an Improved Performance and Perception

 4.1 Introduction

 4.2 The Principle of Infrared Imaging

 4.3 Fusion with Infrared Images

 4.4 Applications

 4.5 Summary

 References

 5 Feature Selection and Ranking for Pattern

 Classification in Wireless Sensor Networks

 5.1 Introduction

 5.2 General Approach

 5.3 Sensor Ranking

 5.4 Experiments

 5.5 Summary, Discussion and Conclusions

 References

 6 Principles and Applications of RIDED-2D-A Robust Edge Detection Method in Range Images

 6.1 Introduction

 6.2 Definitions and Analysis

 6.3 Principles of Instantaneous Denoising and Edge Detection

 6.4 Experiments and Evaluations

 6.5 Discussions and Applications

 6.6 Conclusions and Prospects

 References

Part Ⅱ: Computer Vision and Image Processing

 7 Lens Shading Correction for Dirt Detection

 7.1 Introduction

 7.2 Background

 7.3 Our Proposed Method

 7.4 Experimental Results

 7.5 Conclusions

 References

 8 Using Prototype-Based Classification for Automatic Knowledge Acquisition

 8.1 Introduction

 8.2 Prototype-Based Classification

 8.3 Methodology

 8.4 Application

 8.5 Results

 8.6 Conclusion

 References

 9 Tracking Deformable Objects with Evolving Templates forReal-Time Machine Vision

 9.1 Introduction

 9.2 Problem Formulation

 9.3 Search Framework for Computing Template Position

 9.4 Updating Framework for Computing Template Changes

 ……

Part Ⅲ：Face Recognition and Forensics

Part Ⅳ：Biometric Authentication