Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Understanding human emotions and their nonverbal messages is one of the most necessary and important skills for making the next generation of human-computer interfaces (HCI) easier, more natural and effective. Indeed, the first step toward an automatic emotion sensitive human-computer system having the ability to automatically detect users' nonverbal signals is the development of an accurate and real-time automatic NVC analyzer. Such an analyzer must deal mainly with users' facial expressions and paralanguage. The main goal of this paper is to compare different methods to combine the results of both classifiers. A prototype of the dialog system was developed in the Department of Computer Science. The proposed system is fully automatic, user-independent and real-time working. Several experiments show that the speech recognition quality is increased by using nonverbal information.