Background: Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks.

Facial expressions are important in facilitating human communication and interactions. They are also used as an important tool in behavioral studies and medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for noninvasive mood detection. Most of the previous studies of facial expressions recognition have been based on Facial Action Coding System (FACS) developed by Ekman and Freisen in 1978 and identifying different facial muscular actions. Previous neural network based approaches for classification of facial expressions either have used smaller data bases or used the same data for training and testing. The purpose of the present study was to develop an intelligent system for facial image based expression classification using large data bases. Several facial parameters were extracted from facial image and used to train several generalized and specialized neural networks. The best performing generalized and specialized neural networks were recruited into decision making committees forming an integrated committee neural network system. The integrated committee neural network system was evaluated using data obtained from subjects not used in training or initial testing. The system correctly identified the facial expression in 90.426% of the cases, and represents a significant step forward in correctly identifying the facial expression in large facial expressions database. iv ACKNOWLEDGEMENT It has been a privilege, pleasure and excellent learning experience for me to work with Dr. Narender P. Reddy. It has been an honor to get an opportunity to work under his professional guidance. He has been a constant source of encouragement and support throughout our work together. I take this opportunity to express my sincere and deepest gratitude towards Dr. S.I Hariharan for his constant support and patience. I would also like to thank Dr. Dale Mugler for kindly agreeing to be the part of my committee and guiding me throughout my thesis work. His advice and suggestions have been invaluable in making this project a success. I would also like to thank the faculty of the Department of Biomedical Engineering for their support and guidance. I would like to mention a special thanks to Rick Nemer, for his constant help in all technical problems. A special mention for Bonnie Hinds for all her help and encouragement. I would like to thank my friends Aadithya, Mihir, Nemath, Nikhil and Soniya for their support throughout my stay at the University. This project would have been incomplete without the love and support of my wife Sangeeta and my parents. Their belief in me was a strong motivating factor throughout my work. I owe a lot of my success to them. v TABLE OF CONTENTS .