Titlebook: Handbook of Face Recognition; Stan Z. Li,Anil K. Jain Book 2011Latest edition Springer-Verlag London Limited 2011

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978-1-4471-7119-5Springer-Verlag London Limited 2011

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Zerebrale Aneurysmen und Gef??malformationenThis chapter provides an introduction to face recognition research. Main steps of face recognition processing are described. Face detection and recognition problems are explained from a face subspace viewpoint. Technology challenges are identified after that. Typical strategies for solving the problems are suggested.

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Introduction,This chapter provides an introduction to face recognition research. Main steps of face recognition processing are described. Face detection and recognition problems are explained from a face subspace viewpoint. Technology challenges are identified after that. Typical strategies for solving the problems are suggested.

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Face Subspace Learningral mean criteria and the max-min distance analysis (MMDA) algorithm; manifold learning algorithms, including the discriminative locality alignment (DLA) and manifold elastic net (MEN); and the transfer subspace learning framework. Experiments on face recognition are also provided.

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Local Representation of Facial Featureses to describe faces for recognition, verification, localization, or detection, is a fundamental problem in face biometrics. In this chapter, we review the most popular and successful features for face biometrics. In general, one should include complete algorithms when comparing the features, but ce

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Zerebrale Gef??krankheiten im Alterer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation and apply algebraic and statistical tools for extraction and analysis of the underlying manifold. In this chapter, we describe in roughly chronologic order techniques that identify, para

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https://doi.org/10.1007/978-3-662-10993-9ral mean criteria and the max-min distance analysis (MMDA) algorithm; manifold learning algorithms, including the discriminative locality alignment (DLA) and manifold elastic net (MEN); and the transfer subspace learning framework. Experiments on face recognition are also provided.