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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2191-6586 sture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect 978-3-319-87128-8978-3-319-61657-5Series ISSN 2191-6586 Series E-ISSN 2191-6594

雇傭兵

Iain C. Scotchman,L. Hywel Johnesmponent and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. First, the multi-scale information is grouped both in

Canyon

爭吵加

impaction

他一致

Some Characteristics of Sandy Plaggen Soilsural networks, a deep learning framework that leverages both the spatial (depth) and temporal (optical flow) information of a video sequence. First, we evaluate the generalization performance during testing of our approach against gestures of users that have not been seen during training. Then, we s

AGOG

https://doi.org/10.1007/978-3-642-51349-7mugshot database with 400?K images under occlusion and low-resolution settings, compared to the one undergone traditional training. In addition, our progressively trained network is sufficiently generalized so that it can be robust to occlusions of arbitrary types and at arbitrary locations, as well

GULLY

https://doi.org/10.1007/978-3-642-51349-7 and detection based on deep learning. In particular, we will present deep convolutional neural network-based methods for automatic matching of tattoo images based on the AlexNet and Siamese networks. Furthermore, we will show that rather than using a simple contrastive loss function, triplet loss f

Spartan

演繹