Computer and Robot Vision
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Face Detection and Precise Eyes Location
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
A Factor Tree Inference Algorithm for Bayesian Networks and Its Application
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Automatic Eye Detection and Its Validation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Fuzzy fusion for skin detection
Fuzzy Sets and Systems
Facial event classification with task oriented dynamic Bayesian network
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learner attending auto-monitor in distance learning using image recognition and Bayesian Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Learner attention affects learning efficiency. However, in many classes, teachers cannot assess the degree of attention of every learner. When a teacher is capable of addressing inattentive learners immediately, he can avoid situations in which learners are inattentive. Many studies have analyzed driver attentiveness by the applying of image detection technologies. If this mechanism can be applied to in-class learning, it will help teachers keep learners attentive, and reduce teacher load during class. This study mainly applies fuzzy logic analysis of learner facial images when participating in class. Two fuzzy logic algorithms are proposed to determine the level of inattention by measuring the leaving, drowsiness, head turning and no motion. Applying fuzzy logic can prevent erroneous judgments associated with a single term, and help teachers deal with learner attentiveness. The simulation works are carried to evaluate the effect of the proposed system under various conditions. The simulation results indicated that the proposed system is effective for detecting of learner attentiveness in class.