Unconstrained Face Recognition
Unconstrained Face Recognition by Shaohua Kevin Zhou, published by Springer Science & Business Media on November 30, 2005, is a detailed examination of the advancements in face recognition technology, particularly in unconstrained environments. This edition, comprising 244 pages, delves into the challenges and innovations in biometric recognition, focusing on methods that enhance performance by leveraging the unique characteristics of unconstrained situations.
Readers will find a thorough review of novel approaches to face recognition from video, highlighting the shift from traditional algorithms to more adaptive techniques. The book is tailored for a professional audience, including researchers and industry practitioners, while also being suitable for advanced-level students in computer science. Topics such as computer vision, pattern recognition, and security are explored, providing insights into the evolving landscape of face recognition technology.
Official synopsis Publisher
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.
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