Classifying Emotion based on Facial Expression Analysis using Gabor Filter: A Basis for Adaptive Effective Teaching Strategy

Emotion is equivalent to mood or state of human emotion that correlates with non-verbal behavior. Related literature shows that humans tend to give off a clue for a particular feeling through nonverbal cues such as facial expression. This study aims to analyze the emotion of students using Philippines-based corpus of a facial expression such as fear, disgust, surprised, sad, anger and neutral with 611 examples validated by psychology experts and results aggregates the final emotion, and it will be used to define the meaning of emotion and connect it with a teaching pedagogy to support decisions on teaching strategies. The experiments used feature extraction methods such as Haar-Cascade classifier for face detection; Gabor filter and eigenfaces API for features extraction; and support vector machine in training the model with 80.11% accuracy. The result was analyzed and correlated with the appropriate teaching pedagogies for educators and suggest that relevant interventions can be predicted based on emotions observed in a lecture setting or a class. Implementing the prototype in Java environment, it captured images in actual class to scale the actual performance rating and had an average accuracy of 60.83 %. It concludes that through aggregating the facial expressions of students in the class, an adaptive learning strategy can be developed and implemented in the classroom environment.