Titlebook: Deep Learning-Based Face Analytics; Nalini K Ratha,Vishal M. Patel,Rama Chellappa Book 2021 The Editor(s) (if applicable) and The Author(s

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Deutsche Außenwirtschaftsförderungt of the progress in automatic face recognition has been driven by deep networks in the past few years. In this article, we provide an overview of recent progress in this area and discuss state-of-the-art CNN-based face recognition and verification systems. We also present some open questions and di

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https://doi.org/10.1007/978-3-658-26020-0age synthesis. Conventional 3DMM is learned from a set of 3D face scans with associated well-controlled 2D face images, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as, the linear bases, the representation power of 3DMM can be limited.

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https://doi.org/10.1007/978-3-662-33316-7cial to leverage additional properties of the data to successfully recover the lost facial details in the deblurred image. Priors such as sparsity [., ., .], low-rank [.], manifold [.], and patch similarity [.] have been proposed in the literature to obtain a regularized solution.

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https://doi.org/10.1007/978-3-662-33316-7l-valued feature, often obtained using a deep network. However, comparisons of this high-dimensional feature can be computationally expensive. Furthermore, when dealing with large face images database this representation can lead to prohibitive storage requirements. Also, in a context where the capt

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Empfindsamkeit und Sturm und Drang,e extremely useful. With the help of the right biometric system in place, cases of swapping, for instance, can be evaluated much faster. In this chapter, we first discuss the various biometric modalities along with their advantages and limitations. We next discuss the face biometrics in detail and p

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Darstellung des Untersuchungsmodells,t subject. Various face recognition (FR) systems have been developed over the last two decades. Recent advances in machine learning and computer vision methods have provided robust systems that achieve significant gains in performance of face recognition systems [., .]. Deep learning methods, enable

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