Self Supervised Face Representation Learning
DOWNLOAD
Download Self Supervised Face Representation Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Self Supervised Face Representation Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Self Supervised Face Representation Learning
DOWNLOAD
Author : Vivek Sharma
language : en
Publisher:
Release Date : 2020*
Self Supervised Face Representation Learning written by Vivek Sharma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020* with categories.
Face Recognition Using Self Organising Maps And Semi Supervised Learning
DOWNLOAD
Author : Shireen Mohd Zaki
language : en
Publisher:
Release Date : 2009
Face Recognition Using Self Organising Maps And Semi Supervised Learning written by Shireen Mohd Zaki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Representation Learning And Image Synthesis For Deep Face Recognition
DOWNLOAD
Author : Yin, Xi
language : en
Publisher:
Release Date : 2018
Representation Learning And Image Synthesis For Deep Face Recognition written by Yin, Xi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Electronic dissertations categories.
Deep Learning Based Face Analytics
DOWNLOAD
Author : Nalini K Ratha
language : en
Publisher: Springer Nature
Release Date : 2021-08-16
Deep Learning Based Face Analytics written by Nalini K Ratha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-16 with Computers categories.
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 addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra.
Face Recognition
DOWNLOAD
Author : S. Ramakrishnan
language : en
Publisher: BoD – Books on Demand
Release Date : 2016-07-06
Face Recognition written by S. Ramakrishnan and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-06 with Computers categories.
Pattern recognition has gained significant attention due to the rapid explosion of internet- and mobile-based applications. Among the various pattern recognition applications, face recognition is always being the center of attraction. With so much of unlabeled face images being captured and made available on internet (particularly on social media), conventional supervised means of classifying face images become challenging. This clearly warrants for semi-supervised classification and subspace projection. Another important concern in face recognition system is the proper and stringent evaluation of its capability. This book is edited keeping all these factors in mind. This book is composed of five chapters covering introduction, overview, semi-supervised classification, subspace projection, and evaluation techniques.
Advanced Methods And Deep Learning In Computer Vision
DOWNLOAD
Author : E. R. Davies
language : en
Publisher: Academic Press
Release Date : 2021-11-09
Advanced Methods And Deep Learning In Computer Vision written by E. R. Davies and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-09 with Computers categories.
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field Illustrates principles with modern, real-world applications Suitable for self-learning or as a text for graduate courses
Towards Robust And Secure Face Recognition
DOWNLOAD
Author : Debayan Deb
language : en
Publisher:
Release Date : 2021
Towards Robust And Secure Face Recognition written by Debayan Deb and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Electronic dissertations categories.
The accuracy, usability, and touchless acquisition of state-of-the-art automated face recognition systems (AFR) have led to their ubiquitous adoption in a plethora of domains, including mobile phone unlock, access control systems, and payment services. Despite impressive recognition performance, prevailing AFR systems remain vulnerable to the growing threat of face attacks which can be launched in both physical and digital domains. Face attacks can be broadly classified into three attack categories: (i) Spoof attacks: artifacts in the physical domain (e.g., 3D masks, eye glasses, replaying videos), (ii) Adversarial attacks: imperceptible noises added to probes for evading AFR systems, and (iii) Digital manipulation attacks: entirely or partially modified photo-realistic faces using generative models. Each of these categories is composed of different attack types. For example, each spoof medium, e.g., 3D mask and makeup, constitutes one attack type. Likewise, in adversarial and digital manipulation attacks, each attack model, designed by unique objectives and losses, may be considered as one attack type. Thus, the attack categories and types form a 2-layer tree structure encompassing the diverse attacks. Such a tree will inevitably grow in the future. Given the growing dissemination of "fake news" and "deepfakes", the research community and social media platforms alike are pushing towards generalizable defense against continuously evolving and sophisticated face attacks. In this dissertation, we first propose a set of defense methods that achieve state-of-the-art performance in detecting attack types within individual attack categories, both physical (e.g., face spoofs) and digital (e.g., adversarial faces and digital manipulation), then introduce a method for simultaneously safeguarding against each attack.First, in an effort to impart generalizability and interpretability to face spoof detection systems, we propose a new face anti-spoofing framework specifically designed to detect unknown spoof types, namely, Self-Supervised Regional Fully Convolutional Network (SSR-FCN), that is trained to learn local discriminative cues from a face image in a self-supervised manner. The proposed framework improves generalizability while maintaining the computational efficiency of holistic face anti-spoofing approaches (
Reliable Face Recognition Methods
DOWNLOAD
Author : Harry Wechsler
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-05
Reliable Face Recognition Methods written by Harry Wechsler and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-05 with Computers categories.
This book seeks to comprehensively address the face recognition problem while gaining new insights from complementary fields of endeavor. These include neurosciences, statistics, signal and image processing, computer vision, machine learning and data mining. The book examines the evolution of research surrounding the field to date, explores new directions, and offers specific guidance on the most promising venues for future research and development. The book’s focused approach and its clarity of presentation make this an excellent reference work.
Weakly Supervised Learning For Unconstrained Face Processing
DOWNLOAD
Author : Gary B. Huang
language : en
Publisher:
Release Date : 2012
Weakly Supervised Learning For Unconstrained Face Processing written by Gary B. Huang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Face perception categories.
Machine face recognition has traditionally been studied under the assumption of a carefully controlled image acquisition process. By controlling image acquisition, variation due to factors such as pose, lighting, and background can be either largely eliminated or specifically limited to a study over a discrete number of possibilities. Applications of face recognition have had mixed success when deployed in conditions where the assumption of controlled image acquisition no longer holds. This dissertation focuses on this unconstrained face recognition problem, where face images exhibit the same amount of variability that one would encounter in everyday life. We formalize unconstrained face recognition as a binary pair matching problem (verification), and present a data set for benchmarking performance on the unconstrained face verification task. We observe that it is comparatively much easier to obtain many examples of unlabeled face images than face images that have been labeled with identity or other higher level information, such as the position of the eyes and other facial features. We thus focus on improving unconstrained face verification by leveraging the information present in this source of weakly supervised data. We first show how unlabeled face images can be used to perform unsupervised face alignment, thereby reducing variability in pose and improving verification accuracy. Next, we demonstrate how deep learning can be used to perform unsupervised feature discovery, providing additional image representations that can be combined with representations from standard hand-crafted image descriptors, to further improve recognition performance. Finally, we combine unsupervised feature learning with joint face alignment, leading to an unsupervised alignment system that achieves gains in recognition performance matching that achieved by supervised alignment.
Face Image Analysis By Unsupervised Learning
DOWNLOAD
Author : Marian Stewart Bartlett
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Face Image Analysis By Unsupervised Learning written by Marian Stewart Bartlett and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
