Titlebook: Deep Biometrics; Richard Jiang,Chang-Tsun Li,Christophe Rosenberger Book 2020 Springer Nature Switzerland AG 2020 Deep Learned Biometric.C

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MOTTO

Deep Spectral Biometrics: Overview and Open Issues, wavelength, retaining additional, useful information, beyond that which is recorded by human vision. This can then be exploited against vulnerabilities present in security systems. Further, this additional information can be used by machine learning/computer vision systems for robust personal ident

AGATE

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The Rise of Data-Driven Models in Presentation Attack Detection,hes robust against new attack types (e.g., face morphing)? Do these methods provide other ways to perform PAD, for example, using open-set classifiers rather than the classical binary formulation? Are these methods applicable to the multi-biometric setting? In this chapter, we address these question

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遗传学

2522-848X such as privacy versus security, biometric big data, biometr.This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it “Deep Biometrics”. The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised metho

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https://doi.org/10.1007/978-3-642-96746-7p learning are seemingly making a shift in the optical flow estimation research field. This chapter begins with reviewing traditional (handcrafted) approaches, then introduces the more recent approaches, and finally gets concluded with surveying deep learning approaches.

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Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets, structures are properly set. The use of pairwise neural network structures often improves the performance because such structures require a small set of optimisation parameters. The experiments have been conducted on some face biometric benchmark data sets, and the main findings are presented in the form of a tutorial.