DOI QR코드

DOI QR Code

심층학습 기반 표정인식을 통한 학습 평가 보조 방법 연구

Method of an Assistance for Evaluation of Learning using Expression Recognition based on Deep Learning

  • 이호정 (계명대학교 교육대학원 전산교육전공) ;
  • 이덕우 (계명대학교 공과대학 컴퓨터공학전공)
  • Lee, Ho-Jung (Department of Computer Engineering Education, Graduate School of Education, Keimyung University) ;
  • Lee, Deokwoo (Department of Computer Engineering, Keimyung University)
  • 투고 : 2020.02.03
  • 심사 : 2020.03.15
  • 발행 : 2020.03.31

초록

This paper proposes the approaches to the evaluation of learning using concepts of artificial intelligence. Among various techniques, deep learning algorithm is employed to achieve quantitative results of evaluation. In particular, this paper focuses on the process-based evaluation instead of the result-based one using face expression. The expression is simply acquired by digital camera that records face expression when students solve sample test problems. Face expressions are trained using convolutional neural network (CNN) model followed by classification of expression data into three categories, i.e., easy, neutral, difficult. To substantiate the proposed approach, the simulation results show promising results, and this work is expected to open opportunities for intelligent evaluation system in the future.

키워드

참고문헌

  1. 이요섭.문필주(2017). 딥 러닝 프레임워크의 비교 및 분석. 한국전자통신학회논문지, 12(1), 115-122. https://doi.org/10.13067/JKIECS.2017.12.1.115
  2. 박동건 외(2019). 효과적인 인간-로봇 상호작용을 위한 딥러닝 기반 로봇 비전 자연어 설명문 생성 및 발화 기술. 로봇학회논문지, 14(1), 22-30 https://doi.org/10.7746/jkros.2019.14.1.022
  3. 교육부(2015). 과정을 중시하는 수행평가 어떻게 할까요(초등용, 중등용). 정책정보공표, 1-32.
  4. 정상권 외(2012). 수학적 과정 중심 평가에 대한 교사들의 인식 조사. 수학교육연구, 22(3), 401-427.
  5. 고상숙.박만구.한혜숙(2013). 교구 및 공학도구를 활용한 수학적 과정중심 평가에 관한 교사들의 인식. 한국학교수학회, 16(4), 675-694.
  6. 유진은(2017). 기계학습을 통한 TIMSS 2011 중학생의 수학성취도 관련 변수 탐색. 교원교육, 33(1), 43-56.
  7. Dandil, E., & Ozdemir, R.(2019). Real-time Facial Emotion Classification Using Deep Learning. Data Science and Applications, 2(1), 1-5.
  8. 방건우.신동훈(2017). 인지적 흥미 발생 모형에 따른 초등학생의 얼굴 표정 변화 분석: 생명과학 동영상을 중심으로. 한국초등교육, 28(2), 127-138. https://doi.org/10.20972/KJEE.28.2.201706.127
  9. Bouktif, S., et al.(2018). Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches. Energies, 11(7), 1636, 1-20.
  10. 김인중(2014). Deep Learning: 기계학습의 새로운 트랜드. 한국통신학회지(정보와 통신), 31(11), 52-57.
  11. Alom, M. Z., et al.(2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8(3), 292, 1-67.
  12. Borwarnginn, P., et al.(2019). Breakthrough Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning. Proceedings of the International Conference on Information Technology and Electrical Engineering (ICITEE).
  13. Li, S., & Deng, W.(2018). Deep Facial Expression Recognition: A Survey. arXiv:1804.08348v2, 2018.
  14. He, K., et al.(2016). Deep Residual Learning for Image Recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR) : 770-778.
  15. Visakha, K., & Prakash, S.(2019). Detection and Tracking of Human Beings in a Video Using Haar Classifier. Proceedings of International Conference on Inventive Research in Computing Applications (ICIRCA), 2018.
  16. Correa, E., et al.(2016). Emotion Recognition using Deep Convolutional Neural Network. TU Delft IN4015, 1-12