Puzzlement Detection from Facial Expression Using Active Appearance Models and Support Vector Machines

다국어 초록 (Multilingual Abstract)

Affective state detection, as an emerging field of artificial intelligence, is the key to designing effective natural human-computer interaction, especially for e-learning. It will be helpful to make the computer understand learners’ perceptions and provide appropriate guidance, just like teachers in traditional face-to-face classroom learning. Puzzlement is the most frequent non-neutral affective state in learning, and it is usually a sign that learners need more information and guidance. In this paper, we explore a machine learning approach for puzzlement detection from natural facial expression. We use active appearance models (AAMs) to decouple shape and appearance parameters from the face video sequences. Support vector machines (SVMs) are utilized to classify puzzlement and non-puzzlement with several features derived from AAMs. Using a 10-fold cross validation, we achieve the highest recognition rate of 98.9%. Experimental results indicate the feasibility of automatic frame-level puzzlement detection.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Dataset
• 3. Active Appearance Model
• 3.1. AAM Modeling
• 3.2. AAM Fitting
• 4. Feature Extraction
• 4.1. Pose Normalized Shape
• 4.2. Length Normalized Texture
• 5. SVM Classifiers
• 6. Experiment
• 7. Conclusion
• ACKNOWLEDGEMENTS
• References