Titlebook: Deep Learning for Biometrics; Bir Bhanu,Ajay Kumar Book 2017 Springer International Publishing AG, part of Springer Nature 2017 Deep Learn

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Learning Representations for Cryptographic Hash Based Face Template Protectioning ability of neural networks has enabled them to achieve state-of-the-art results in several fields, including face recognition. Consequently, biometric authentication using facial images has also benefited from this, with deep convolutional neural networks pushing the matching performance numbers

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Deep Triplet Embedding Representations for Liveness Detectionr an attacker it is relatively easy to build a fake replica of a legitimate finger and apply it directly to the sensor, thereby fooling the system by declaring its corresponding identity. In order to ensure that the declared identity is genuine and it corresponds to the individual present at the tim

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Gender Classification from NIR Iris Images Using Deep Learning methods to separate the gender-from-iris images even when the amount of learning data is limited, using an unsupervised stage with Restricted Boltzmann Machine (RBM) and a supervised stage using a Convolutional Neural Network (CNN).

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Book 2017ual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also exa

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2191-6586 arning integrated biometric techniques, including face, fingThis timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on t