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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Book 2020o highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emer

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https://doi.org/10.1007/978-3-540-36693-5e attention of the network to image regions that are descriptive and important for representation purposes. The model is trained in an end-to-end manner using a combined cross-entropy and center loss. Extensive experiments on the recently introduced Extended Annotated Web Ears (AWEx).

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Anti-Spoofing in Face Recognition: Deep Learning and Image Quality Assessment-Based Approaches,ecture that combines a pre-trained CNN model and the local binary patterns (LBP) descriptor. Both approaches are extensively evaluated on different datasets. The obtained results outperformed state-of-the-art approaches. Moreover, our methods are well suited for real-time mobile applications and they are also privacy-compliant.

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Constellation-Based Deep Ear Recognition,e attention of the network to image regions that are descriptive and important for representation purposes. The model is trained in an end-to-end manner using a combined cross-entropy and center loss. Extensive experiments on the recently introduced Extended Annotated Web Ears (AWEx).