	Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop (Author) The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of...
	Pixelization Paradigm: Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers (Lecture Notes in Computer ... Vision, Pattern Recognition, and Graphics) by Pierre P Lévy (Editor), Bénédicte Le Grand (Editor), François Poulet (Editor), Michel Soto (Editor), Laszlo Darago (Editor), Laurent Toubiana (Editor), Jean-Francois Vibert (Editor) This book constitutes the thoroughly refereed post-proceedings of the Visual Information Expert Workshop, VIEW 2006, held in Paris, France, in April 2006. The 23 revised full papers were carefully selected from numerous submissions during two rounds of reviewing and improvement. The book is categorized in three main parts: pixelization theory, pixelization applications, pixelization and cognition. The papers are organized in topical sections on pixelization, pixelization and multidimensional...
	Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR ... Vision, Pattern Recognition, and Graphics) by Rainer Stiefelhagen (Editor), John Garofolo (Editor) This book constitutes the thoroughly refereed post-proceedings of the First International CLEAR 2006 Evaluation Campaign and Workshop on Classification of Events, Activities and Relationships for evaluation of multimodal technologies for the perception of humans, their activities and interactions, held in Southampton, UK, in April 2006. The 29 revised full system description papers and 1 institutional paper presented together with 2 invited papers were carefully reviewed and selected for...

EDITORIAL REVIEWS

Product Description
One of the grand challenges for computational intelligence and biometrics is to understand how people process and recognize faces and to develop automated and reliable face recognition systems. Biometrics has become the major component in the complex decision making process associated with security applications. The many challenges addressed for face detection and authentication include cluttered environments, occlusion and disguise, temporal changes, and last but not least, robust training and open set testing. Reliable Face Recognition Methods seeks to comprehensively address the face recognition problem while drawing inspiration and gaining new insights from complementary fields of endeavor such as neurosciences, statistics, signal and image processing, computer vision, and 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 R&D. With its well-focused approach and clarity of presentation, this new text/reference is an excellent resource for computer scientists and engineers, researchers, and professionals who need to learn about face recognition. In addition, the book is ideally suited to students studying biometrics, pattern recognition, and human-computer interaction.

CUSTOMER REVIEWS (Average Customer Rating: 4.0 based on 1 review)

much more work needed by W Boudville (Terra, Sol 3)

4 Stars
December 29, 2006
The problem tackled by this book is buried deep in evolution. It is one of the most basic properties of an organism that it reliably recognise others of its species. For reproduction, if nothing else. Humans have the added twist that facial recognition is important, for the extra reasons that the face is usually hairless and has many expressive muscles. But the way the mind does its analysis is still largely opaque. By contrast, most of the book's methods look at it via other approaches. Signal processing with many custom features specific to faces. The chapters show impressive current capabilities. However, they also readily indicate the limitations. And suggest possible future improvements.

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