DOI: 10.4018/IJSSCI.2019010101 International Journal of Software Science and Computational Intelligence Volume 11 • Issue 1 • January-March 2019 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 1 Unobtrusive Academic Emotion Recognition Based on Facial Expression Using RGB-D Camera Using Adaptive-Network-Based Fuzzy Inference System (ANFIS) James Purnama, Swiss German University, Indonesia Riri Fitri Sari, University of Indonesia, Depok, Indonesia ABSTRACT Quality of learning in the classroom is influenced by many factors. One of them is the academic emotions of the students. The emotion detection in the classroom cannot be done by using sensors attached to the body of the students, because it would disturb the concentration of the students. The proposed solution is by using unobtrusive emotion detection, e.g. by placing video capture equipment, which is not visible at the front of the student’s desk. In this study, an RGB - Depth Microsoft Kinect camera is used to record facial expressions by considering the convenience factor of the students, speed of response time, and cost efficiency. A combination of Cohn-Kanade dataset and EURECOM dataset is used as the training set in machine learning with Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithm, with 8 sample of Asian race students (4 male and 4 female students). KEywoRDS Academic Emotion Detection, ANFIS, Facial Expression, RGB-D Camera INTRoDUCTIoN In the era of Information Technology, the teaching and learning process in the classroom is still the main method of learning. In addition, there are e-learning methods and Intelligent Tutoring System, which are supplements to the classroom learning method. Quality of learning in the classroom is influenced by many aspects, one of which is the Academic Emotions. Pekrun et al. (2002) describes that the Academic Emotions can affect learning qualities which confidence, excitement, frustration, interest, flow/engagement, boredom, confusion, and anxiety (Arroyo et al., 2009; Azcarraga et al., 2011; Burleson, 2006; Azcarraga et al., 2010; Kapoor et al., 2007; Zeidner, 2007). Previously, emotion detection used sensor devices that are plugged / affixed to the body of the research subject. The sensor devices are physiological signals or biosignal sensors to measure body signals, such as Electromyography (EMG), Electro Dermal Activity (EDA) or Galvanic Skin Response (GSR). This method is commonly referred to as the Hidden Ways (covertly). This method was perceived only suitable for testing in the laboratory, and are not effective when implemented in