Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cascaded Classification of Gender and Facial Expression using Active Appearance Models
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Facial feature detection using Haar classifiers
Journal of Computing Sciences in Colleges
Facial Expression Recognition for E-learning Systems using Gabor Wavelet & Neural Network
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Facial Expression Recognition Based on Fusion of Multiple Gabor Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Dynamic interactive VR network services for education
Proceedings of the ACM symposium on Virtual reality software and technology
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
e5Learning, an E-Learning Environment Based on Eye Tracking
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
An Expression Space Model for Facial Expression Analysis
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Facial Expression Recognition Based on LBP-EHMM
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
A Mixed Classifier Based on Combination of HMM and KNN
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
A Natural Facial Expression Recognition Using Differential-AAM and k-NNS
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
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Facial expression provides an important clue for teachers to know the learning status of students. Thus, vision-based expression analysis is valuable not only in Human-Computer Interface but also in e-Learning. We propose a computer vision system to automatically analyze learners' video to recognize nonverbal facial expressions to discover learning status of students in distance education. In the first stage, Adaboost classifiers are applied to extract candidates of facial parts. Then spatial relationships are utilized to determine the best combination of facial features to form a feature vector. In the second stage, each feature vector sequence is trained and recognized as a specific emotional expression using Hidden Markov Model (HMM). The estimated probabilities of six expressions are combined into an expression vector. The last stage is to analyze the expression vector sequence to figure out the learning situation of the student. Gaussian Mixture Model (GMM) is applied to evaluate three learning scores (Understanding, Interaction, and Consciousness) that are integrated into a status vector. Each evaluated status vector reflects the learning status of a student and is helpful to not only teachers but also students for improving teaching and learning.