Название: Handbook of Face Recognition: The Deep Neural Network Approach, 3rd EditionАвтор: Stan Z. Li, Anil K. Jain, Jiankang Deng
Издательство: Springer
Год: 2024
Страниц: 473
Язык: английский
Формат: pdf (true)
Размер: 12.7 MB
Over the past decade, Deep Learning has emerged as a powerful tool for solving a wide range of complex problems in Computer Vision, speech recognition, and Natural Language Processing (NLP). One area where Deep Learning has shown particularly promising results is in face recognition.
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.
Face recognition is a critical technology with applications in security, surveillance, biometrics, and human-computer interaction. Deep learning-based approaches have achieved state-of-the-art performance in face recognition tasks, enabling accurate and efficient recognition of faces in a variety of settings.
This handbook brings together some of the leading experts in the field of deep learning-based face recognition to provide a comprehensive overview of the current state of the art. The chapters cover a broad range of topics, such as Deep Learning fundamentals, face detection, facial landmark localization, facial attribute analysis, face presentation attack detection, face feature embedding, video-based face recognition, face recognition with synthetic data, uncertainty-aware face recognition, reducing bias in face recognition, adversarial attacks on face recognition, heterogeneous face recognition, and 3D face recognition.
This book serves as an all-encompassing resource, providing theoretical underpinnings, algorithms, and implementations to guide students, researchers, and practitioners across all aspects of face recognition. In addition to showcasing the most recent advancements in methods and algorithms, the book also supplies code and data to facilitate hands-on learning and the creation of reproducible face recognition algorithms and systems (Appendix) through Deep Learning programming. The code and data will be accessible on GitHub and will be updated regularly to keep the materials up to date.
The Chapter 2 briefly introduces Convolutional Neural Networks (CNNs). One of the first CNNs is proposed in LeCun, Y. "Gradient-based learning applied to document recognition" (known as LeNet) to deal with handwriting recognition task. After that, CNN becomes the most popular deep neural network model to process visual data, including images and videos. Until now, a lot of modern fundamental computer vision systems, e.g., image classification and face recognition, are usually built upon convolutional networks.
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 deep learning-based face recognition, image processing, computer vision, biometrics, security, Internet, mobile devices, human-computer interface, E-services, computer graphics and animation, and the computer game industry. It provides a comprehensive overview of the field, from the fundamentals to the latest advances, and offers guidance on how to develop and deploy these technologies in a responsible and ethical manner.
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