Instead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition.

Autorentext
S. M. Mahbubur Rahman received his Ph.D. in Electrical and Computer Engineering (ECE) from Concordia University, Montreal, QC, Canada, in 2009. Currently, he serves as a Professor at the Department of Electrical and Electronic Engineering (EEE), Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh. In 2017, he was a Visiting Research Professor of the EEE at the University of Liberal Arts Bangladesh. He was tenured at the University of Toronto as an NSERC (Natural Sciences and Engineering Research Council) Postdoctoral Fellow in 2012. He has a strong record of research in his area, which includes contributing to more than 50 publications in SCI-indexed journals and the peer-reviewed proceedings of international conferences. He has served as an Associate Editor for an SCI-indexed journal: Circuits, Systems, and Signal Processing, published by Springer Nature. His research interests are in the areas of biometric security systems, intelligent transportation systems, cognitive science, stereo vision, virtual reality, biomedical visualization, humancomputer interaction, video surveillance, signal processing and communication systems. Tamanna Howlader received her Ph.D. in Mathematics from Concordia University, Canada. Currently, she is a Professor of Applied Statistics at the Institute of Statistical Research and Training (ISRT), University of Dhaka, Bangladesh. She is a statistician who enjoys interdisciplinary research. The articles and book chapters that she has published demonstrate novel statistical applications in the areas of image processing, computer vision, pattern recognition and public health. Tamanna has received several prestigious awards including the Sydney R. Parker Best Paper Award from the Journal of Circuits, Systems and Signal Processing published by Springer Nature. She is a member of the International Statistical Institute. Dimitrios Hatzinakos received his Ph.D. in Electrical Engineering from Northeastern University, Boston, MA, in 1990, and currently serves as a Professor at the Department of Electrical and Computer Engineering, University of Toronto (UofT), Toronto, Canada. He is the co-founder and since 2009 the Director and the Chair of the management committee of the Identity, Privacy and Security Institute (IPSI) at the UofT. His research interests and expertise are in the areas of multimedia signal processing, multimedia security, multimedia communications and biometric systems. He is the author/co-author of more than 300 papers in technical journals and conference proceedings; he has contributed to 18 books, and he holds seven patents in his areas of interest. He is a Fellow of the IEEE, a Fellow of the Engineering Institute of Canada, and a member of the Professional Engineers of Ontario, and the Technical Chamber of Greece.

Inhalt

1 Introduction.- 2 Image Moments.- 3 Face Recognition.- 4 Expression Recognition.- 5 Fingerprint Classi cation.- 6 Iris Recognition.- 7 Hand Gesture Recognition.- 8 Conclusion.