The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAUI: a multimodal affective user interface
Proceedings of the tenth ACM international conference on Multimedia
Multi-Modal Tracking of Faces for Video Communications
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
PersonSpotter - Fast and Robust System for Human Detection, Tracking and Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Spotting Segments Displaying Facial Expression from Image Sequences Using HMM
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
An Expert System for Multiple Emotional Classification of Facial Expressions
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction
A Framework for Modeling Appearance Change in Image Sequences
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Probabilistic multiple face detection and tracking using entropy measures
IEEE Transactions on Circuits and Systems for Video Technology
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Despite increasing so called robust algorithms have been proposed, classifying facial expressions in real-time systems, especially in e-learning, is a different story and not an easy job. Some influential aspects such as machine and hardware limitation, environmental issues, and even suitable expressions (from students) categories for e-learning systems are often neglected. In this paper, we discuss extensively on these problematic yet neglected issues and study for the possible, existing methods to handle them for an e-learning system. For some special cases, we suggest for alternatives other than facial expressions analysis, which may help the system to perform better in terms of user modeling. The paper is concluded with suggestion for more advanced and feasible analysis of studentýs other behaviors (despite facial expressions) with the idea that no additional input device should be put on studentýs body, which can cause uneasy feel on them.