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Soft Computing

, Volume 22, Issue 9, pp 2973–2999 | Cite as

Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning

  • Hui-Chuan Chu
  • William Wei-Jen Tsai
  • Min-Ju Liao
  • Yuh-Min Chen
Methodologies and Application
  • 464 Downloads

Abstract

Emotions deeply affect learning achievement. In the case of students with high-functioning autism (HFA), negative emotions such as anxiety and anger can impair the learning process due to the inability of these individuals to control their emotions. Attempts to regulate negative emotions in HFA students once they have occurred, subsequent regulation to HFA students is often ineffective because it is difficult to calm them down. Hence, detecting emotional transitions and providing adaptive emotional regulation strategies in a timely manner to regulate negative emotions can be especially important for students with HFA in an e-learning environment. In this study, a facial expression-based emotion recognition method with transition detection was proposed. An emotion elicitation experiment was performed to collect facial-based landmark signals for the purpose of building classifiers of emotion recognition. The proposed method used sliding window technique and support vector machine (SVM) to build classifiers in order to recognize emotions. For the purpose of determining robust features for emotion recognition, Information Gain (IG) and Chi-square were used for feature evaluations. The effectiveness of classifiers with different parameters of sliding windows was also examined. The experimental results confirmed that the proposed method has sufficient discriminatory capability. The recognition rates for basic emotions and transitional emotions were 99.13 and 92.40%, respectively. Also, through feature selection, training time was accelerated by 4.45 times, and the recognition rates for basic emotions and transitional emotions were 97.97 and 87.49%, respectively. The method was applied in an adaptive e-learning environment for mathematics to demonstrate its application effectiveness.

Keywords

Students with autism Mathematics e-learning Human–computer interface Emotion recognition Emotional transition 

Notes

Compliance with ethical standards

Conflict of Interest

Hui-Chan Chu declares that she has no conflict of interest. William Wei-Jen Tsai declares that he has no conflict of interest. Min-Ju Liao declares that she has no conflict of interest. Yuh-Min Chen declares that he has no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Hui-Chuan Chu
    • 1
  • William Wei-Jen Tsai
    • 2
  • Min-Ju Liao
    • 3
  • Yuh-Min Chen
    • 2
  1. 1.Department of Special EducationNational University of TainanTainan CityTaiwan, ROC
  2. 2.Institute of Manufacturing Information and SystemsNational Cheng Kung UniversityTainan CityTaiwan, ROC
  3. 3.Department of PsychologyNational Chung-Cheng UniversityTainan CityTaiwan, ROC

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