Deep Learning-Based Face Analytics

This book provides an overview of different deep learning-based methods for face recognition and related problems, covering topics such as face detection, localization, tracking, recognition, and more. It is aimed at graduate students studying electrical engineering and computer science and can be used as a textbook for teaching biometrics-related courses.

Format: Paperback / softback
Length: 407 pages
Publication date: 18 August 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the realm of face recognition, offering a thorough exploration of diverse deep learning-based methods employed for tackling various related challenges. The authors present a comprehensive overview of techniques based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks, highlighting their applications in face detection, localization, tracking, recognition, and more. Furthermore, they critically analyze the strengths and weaknesses of existing approaches, identifying promising avenues for future research in this rapidly evolving field.

Designed primarily for graduate students pursuing studies in electrical engineering and computer science, this book serves as a valuable resource for imparting knowledge on face recognition-related topics. It can also be effectively utilized as a textbook for courses specializing in biometrics, a subject widely offered at both undergraduate and graduate levels across numerous institutions worldwide.

The prerequisites for optimal utilization of this book include a solid foundation in pattern recognition, machine learning, probability theory, and linear algebra. By leveraging these foundational concepts, readers can gain a deep understanding of the principles and techniques employed in face recognition, enabling them to apply them to real-world scenarios and contribute to the ongoing advancements in this field.

Weight: 635g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030746995
Edition number: 1st ed. 2021