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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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书目名称Deep Learning-Based Face Analytics
编辑Nalini K Ratha,Vishal M. Patel,Rama Chellappa
视频video
概述Is First compiled source on deep learning applied to face image and video analytics.Reflects on Bias in face analytic algorithms using AI methods.Explores on Deepfake attacks in face recognition.Compa
丛书名称Advances in Computer Vision and Pattern Recognition
图书封面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
描述.This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods 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 evolving field...Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition...This book is aimed at graduate students studying electrical engineering and/or computer science.  Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In ad
出版日期Book 2021
关键词Deep Learning; AI Technique in Face Analysis; Face Recognition; Facial Expression Analysis; Face Detecti
版次1
doihttps://doi.org/10.1007/978-3-030-74697-1
isbn_softcover978-3-030-74699-5
isbn_ebook978-3-030-74697-1Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-3-642-95770-3with only 8% labeling, we can achieve performance very close to that with full-set labeling. In the second problem, we focus on the size of the camera network and consider how to onboard new cameras into an existing network with little to no additional supervision. We leverage upon transfer learning
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Zivil-militärische Zusammenarbeitgorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method reveals biases due to gender, hair length, age, and facial hair. We also show that our synthetic transects allow for a more straightforward bias analysi
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