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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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11#
發(fā)表于 2025-3-23 10:54:01 | 只看該作者
https://doi.org/10.1007/978-3-662-33316-7ure of the face image is performed on a mobile device or in a separate location from the face verification?or search process, the amount of data that needs to be transmitted over the network should be minimized.
12#
發(fā)表于 2025-3-23 13:51:50 | 只看該作者
13#
發(fā)表于 2025-3-23 18:04:30 | 只看該作者
https://doi.org/10.1007/978-3-658-41970-7in how humans with various levels of expertise approach face identification tasks. We conclude by considering the challenging problem of human and machine performance on recognition of faces of different races. Understanding how humans and machines perform these tasks can lead to more effective and accurate face recognition in applied settings.
14#
發(fā)表于 2025-3-24 01:46:18 | 只看該作者
Book 2021methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolvi
15#
發(fā)表于 2025-3-24 05:06:43 | 只看該作者
Empfindsamkeit und Sturm und Drang,w born face recognition. Finally, we evaluate and compare these techniques. Our comparative analysis shows that the state-of-the-art SSF-CNN technique achieves an average of rank-1 new born accuracy of ..
16#
發(fā)表于 2025-3-24 08:41:17 | 只看該作者
17#
發(fā)表于 2025-3-24 13:07:26 | 只看該作者
18#
發(fā)表于 2025-3-24 17:17:18 | 只看該作者
Thermal-to-Visible Face Synthesis and Recognition,ents in face recognition accuracy, particularly in unconstrained scenarios?[., ., ., ., .]. Also, largely driven by social network companies, progress in face recognition research, development, and deployment have focused on faces collected in visible regimes of the electromagnetic spectrum.
19#
發(fā)表于 2025-3-24 19:49:45 | 只看該作者
20#
發(fā)表于 2025-3-25 00:09:56 | 只看該作者
Obstructing DeepFakes by Disrupting Face Detection and Facial Landmarks Extraction,action?method with specially designed imperceptible adversarial perturbations?to reduce the quality of the detected faces. We empirically show the effectiveness of our methods in disrupting state-of-the-art DNN-based face detectors and facial landmark extractors on several datasets.
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