Automatic Facial Expression Recognition Using Artificial Neural Networks

Automatic Facial Expression Recognition Using Artificial Neural Networks

New Research in Multimedia and Internet Systems

The rapid proliferation of Multimedia and Network Information Systems is one of the key features of our times. What is also important is that the pace of change is ever increasing. University projects of today will form the core of consumer products of tomorrow. Therefore, it is very important to have a broad view of the recent scientific investigation in that area. This was the primary reason for gathering this collection of carefully selected and hopefully representative research projects, found solutions, and finally applications. They are the achievements of scientific teams from several countries. The contents of the monograph has been divided into four parts: 1) Multimedia Information Technology, 2) Information Systems Specification, 3) Information Systems Applications, 4) Web Systems and Network Technologies. The book presents up to date research from the diverse fields of multimedia and Internet data processing.

Machine Learning Techniques for Multimedia

Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with machine learning in multimedia applications.