Titlebook: Machine-based Intelligent Face Recognition; Dengpan Mou Book 2010Latest edition Springer-Verlag Berlin Heidelberg 2010 Face Recognition.HE

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书目名称Machine-based Intelligent Face Recognition影响因子(影响力)




书目名称Machine-based Intelligent Face Recognition影响因子(影响力)学科排名




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书目名称Machine-based Intelligent Face Recognition网络公开度学科排名




书目名称Machine-based Intelligent Face Recognition被引频次




书目名称Machine-based Intelligent Face Recognition被引频次学科排名




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书目名称Machine-based Intelligent Face Recognition年度引用学科排名




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书目名称Machine-based Intelligent Face Recognition读者反馈学科排名

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Springer-Verlag Berlin Heidelberg 2010

过分

Dengpan MouDiscusses the general engineering methods for imitating intelligent human brains for video-based face recognition that is completely unsupervised, self-adaptive and robust.All cues are combined togeth

纪念

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https://doi.org/10.1007/978-3-642-00751-4Face Recognition; HEP; Intelligent Face Recognition; Self-adaptive; Self-learning

FIS

Introduction,ing point of this book, we are discussing the fundamental pros and cons of state-of-the-art machine vision system versus its human being counterpart. Inspired from the comparison, in the second part of this chapter, a machine-based intelligent face recognition system is briefly introduced. With the

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Fundamentals and Advances in Biometrics and Face Recognition,ures and categories are defined and overviewed. This is followed in section 2 by the brief introduction and surveys on current cognitive science research. The essential point here is the fundamental intelligence of human brains. In section 3, machine-based biometric recognition tasks and methods are

支柱

Combined Face Detection and Tracking Methods,ep for an intelligent, automatic and unsupervised face recognition system. In section 1, the proposals of face detection are briefly overviewed. Imagedbased face detection issues are discussed in section 2, including the choice of detection algorithms, the definition of face region based on the eye

火光在摇曳

Automatic Face Recognition,n-related sub-steps of the enrollment procedure, including same face decision, feature extraction and encoding. If each single step is running passively, the autonomous procedure of recognition is guaranteed. After the introduction section, feature extraction and encoding methods are briefly covered

欢腾

Unsupervised Face Database Construction,arning background is introduced in section 2, including supervised as well as unsupervised learning methods, and the analysis of typical clustering structures. In section 3, a proposed data structure is described, where the fused clustering structure and its related parameters for building the face