Towards A Robust Unconstrained Face Recognition Pipeline With Deep Neural Networks
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Towards A Robust Unconstrained Face Recognition Pipeline With Deep Neural Networks
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Author : Yichun Shi
language : en
Publisher:
Release Date : 2021
Towards A Robust Unconstrained Face Recognition Pipeline With Deep Neural Networks written by Yichun Shi 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.
Face recognition is a classic problem in the field of computer vision and pattern recognition due to its wide applications in real-world problems such as access control, identity verification, physical security, surveillance, etc. Recent progress in deep learning techniques and the access to large-scale face databases has lead to a significant improvement of face recognition accuracy under constrained and semi-constrained scenarios. Deep neural networks are shown to surpass human performance on Labeled Face in the Wild (LFW), which consists of celebrity photos captured in the wild. However, in many applications, e.g. surveillance videos, where we cannot assume that the presented face is under controlled variations, the performance of current DNN-based methods drop significantly. The main challenges in such an unconstrained face recognition problem include, but are not limited to: lack of labeled data, robust face normalization, discriminative representation learning and the ambiguity of facial features caused by information loss.In this thesis, we propose a set of methods that attempt to address the above challenges in unconstrained face recognition systems. Starting from a classic deep face recognition pipeline, we review how each step in this pipeline could fail on low-quality uncontrolled input faces, what kind of solutions have been studied before, and then introduce our proposed methods. The various methods proposed in this thesis are independent but compatible with each other. Experiment on several challenging benchmarks, e.g. IJB-C and IJB-S show that the proposed methods are able to improve the robustness and reliability of deep unconstrained face recognition systems. Our solution achieves state-of-the-art performance, i.e. 95.0% TAR FAR=0.001% on IJB-C dataset and 61.98% Rank1 retrieval rate on the surveillance-to-booking protocol of IJB-S dataset.
Deep Learning Based Face Analytics
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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.
Scale Aware Multi Path Deep Neural Networks For Unconstrained Face Detection
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Author : Yuguang Liu
language : en
Publisher:
Release Date : 2017
Scale Aware Multi Path Deep Neural Networks For Unconstrained Face Detection written by Yuguang Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
"Unconstrained face detection is the task of robustly finding and locating faces in an image subject to possible variations in facial scale, blur, pose, illumination, occlusion, and facial expression. It is a critical first step towards a host of modern surveillance applications, including but not limited to face verification, face recognition, face tracking, and human-computer interaction. Though much progress has been made in unconstrained face detection during the past decade, the majority of work focuses on improving the detection robustness on variations caused by blur, pose, illumination, occlusion and facial expression. Facial scale, despite its immense influence on face detection accuracy, has received much less attention than have the above factors. This is partially due to the fact that most traditional face detection benchmark datasets tend to collect faces of relatively large size and with modest scale variation. Nonetheless, in real-world applications, such as surveillance systems, it is imperative to possess an equal ability to detect both big faces (close to camera) and tiny ones (far away from the camera) at the same time. To the best of our knowledge, no published face detection algorithm can detect a face as large as 1000 x 1000 pixels while simultaneously detecting another one as small as 10 x 10 pixels within a single image with similarly high accuracy.We introduce a Multi-Path Face Detection Network (MP-FDN) to filter an image for simultaneously proposing and verifying different sized faces in parallel paths. This is the first time that faces across a large span of scales are detected by a single network with forked detection paths. More importantly, the division of the paths are not handcrafted, but totally based on the scale sensitivity inherent in the convolutional networks that was also discovered in this thesis for the first time. MP-FDN consists of two stages. The first stage is a Multi-Path Face Proposal Network (MP-FPN) that suggests faces at three different scale ranges. This design is based on our observation that the hierarchical multi-scale layers of deep convolutional networks (ConvNet) can inherently represent face patterns at multiple scales. In particular, low-level ConvNet layers are more sensitive to tiny faces, while high-level ConvNet layers are more discriminative to big faces. To this end, MP-FPN utilizes three parallel outputs of the convolutional feature maps to simultaneously predict small, medium and large candidate face regions, respectively. The second stage is a Multi-Path Face Verification Network (MP-FVN) that further eliminates false positives while including false negatives. MP-FVN utilizes the same three parallel paths as MP-FPN. For each detection path, it pools features from both a face candidate region (provided by MP-FPN) and a larger contextual region (surrounding the face candidate region). These facial and contextual features are then concatenated to provide a more accurate "faceness" probability to the face candidate. Note that the network structure and hyper-parameters of MP-FPN and MP-FVN are completely based on controlled experiments, rather than being "handcrafted". To testify to the performance of MP-FDN on the basis its ability to perform face detection, we conducted comprehensive experiments on two challenging public face detection benchmark datasets: WIDER FACE and FDDB datasets. MP-FDN consistently achieves better than the state-of-the-art performance on both of them. Specifically, on the most challenging so-called "hard partition" of WIDER FACE test set that contains faces as small as about 9 pixels and as large as more than 1000 pixels in height, MP-FDN outperforms the former best result by 9.8% for the Average Precision. This demonstrates that MP-FDN is a viable and accurate face detector for unconstrained face detection, especially in the case of large scale variations." --
Unconstrained Face Recognition
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Author : Shaohua Kevin Zhou
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-10-11
Unconstrained Face Recognition written by Shaohua Kevin Zhou 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 2006-10-11 with Computers categories.
Face recognition has been actively studied over the past decade and continues to be a big research challenge. Just recently, researchers have begun to investigate face recognition under unconstrained conditions. Unconstrained Face Recognition provides a comprehensive review of this biometric, especially face recognition from video, assembling a collection of novel approaches that are able to recognize human faces under various unconstrained situations. The underlying basis of these approaches is that, unlike conventional face recognition algorithms, they exploit the inherent characteristics of the unconstrained situation and thus improve the recognition performance when compared with conventional algorithms. Unconstrained Face Recognition is structured to meet the needs of a professional audience of researchers and practitioners in industry. This volume is also suitable for advanced-level students in computer science.
Handbook Of Face Recognition
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Author : Stan Z. Li
language : en
Publisher: Springer Nature
Release Date : 2024-01-30
Handbook Of Face Recognition written by Stan Z. Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-30 with Computers categories.
The history of computer-aided face recognition dates to the 1960s, yet the problem of automatic face recognition – a task that humans perform routinely and effortlessly in our daily lives – still poses great challenges, especially in unconstrained conditions. This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational recognition systems. After a thorough introduction, each subsequent chapter focuses on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Topics and features: Fully updated, revised, and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated detection and recognition systems Provides comprehensive coverage of face detection, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications Contains numerous step-by-step algorithms Describes a broad range of applications from person verification, surveillance, and security, to entertainment Presents contributions from an international selection of preeminent experts Integrates numerous supporting graphs, tables, charts, and performance data This practical and authoritative reference is an essential resource for researchers, professionals and students involved in image processing, computer vision, biometrics, security, Internet, mobile devices, human-computer interface, E-services, computer graphics and animation, and the computer game industry.
Texture Based 3d Face Recognition Using Deep Neural Networks For Unconstrained Human Machine Interaction
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Author : Michael Danner
language : en
Publisher:
Release Date : 2020
Texture Based 3d Face Recognition Using Deep Neural Networks For Unconstrained Human Machine Interaction written by Michael Danner 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.
Development Of A Deep Learning Scheme For Unconstrained Face Recognition
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Author : Marwa Yousif Hassan Said
language : en
Publisher:
Release Date : 2016
Development Of A Deep Learning Scheme For Unconstrained Face Recognition written by Marwa Yousif Hassan Said and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Face Image Analysis With Convolutional Neural Networks
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Author : Stefan Duffner
language : en
Publisher: GRIN Verlag
Release Date : 2009-08
Face Image Analysis With Convolutional Neural Networks written by Stefan Duffner and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08 with Computers categories.
Doctoral Thesis / Dissertation from the year 2008 in the subject Computer Science - Applied, grade: 1, University of Freiburg (Lehrstuhl für Mustererkennung und Bildverarbeitung), language: English, abstract: In this work, we present the problem of automatic appearance-based facial analysis with machine learning techniques and describe common specific sub-problems like face detection, facial feature detection and face recognition which are the crucial parts of many applications in the context of indexation, surveillance, access-control or human-computer interaction. To tackle this problem, we particularly focus on a technique called Convolutional Neural Network (CNN) which is inspired by biological evidence found in the visual cortex of mammalian brains and which has already been applied to many different classi fication problems. Existing CNN-based methods, like the face detection system proposed by Garcia and Delakis, show that this can be a very effective, efficient and robust approach to non-linear image processing tasks. An important step in many automatic facial analysis applications, e.g. face recognition, is face alignment which tries to translate, scale and rotate the face image such that specific facial features are roughly at predefined positions in the image. We propose an efficient approach to this problem using CNNs and experimentally show its very good performance on difficult test images. We further present a CNN-based method for automatic facial feature detection. The proposed system employs a hierarchical procedure which first roughly localizes the eyes, the nose and the mouth and then refines the result by detecting 10 different facial feature points. The detection rate of this method is 96% for the AR database and 87% for the BioID database tolerating an error of 10% of the inter-ocular distance. Finally, we propose a novel face recognition approach based on a specific CNN architecture learning a non-linear mapping of the image space into a lower-dim
Practical Machine Learning And Image Processing
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Author : Himanshu Singh
language : en
Publisher: Apress
Release Date : 2019-02-26
Practical Machine Learning And Image Processing written by Himanshu Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-26 with Computers categories.
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will LearnDiscover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
Deep Learning For Computer Vision
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2019-04-04
Deep Learning For Computer Vision written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-04 with Computers categories.
Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
