한국인터넷방송통신학회논문지 (The Journal of the Institute of Internet, Broadcasting and Communication)

  • 제18권1호
  • /
  • Pages.113-117
  • /
  • 2018
  • /
  • 2289-0238(pISSN)
  • /
  • 2289-0246(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
권호 표지
DOI QR코드

DOI QR Code

적응적 딥러닝 학습 기반 영상 인식

Image Recognition based on Adaptive Deep Learning

  • 김진우 (인하대학교 컴퓨터공학부) ;
  • 이필규 (인하대학교 컴퓨터공학부)
  • 투고 : 2017.11.28
  • 심사 : 2018.02.09
  • 발행 : 2018.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

사람의 감정은 다양한 요소에 의해서 드러난다. 말, 행동, 표정, 옷차림 등등. 하지만 사람은 자신의 감정을 숨길 줄 안다. 따라서 어느 한 가지만으로는 쉽게 그 감성을 짐작할 수 없다. 우리는 이러한 문제를 해결하고 보다 진솔한 사람의 감성을 파악하기 위해 행동과 표정에 주의를 기울이기로 하였다. 행동과 표정은 부단한 노력과 훈련이 없으면 쉽게 감출 수 없기 때문이다. 본 논문에서는 딥러닝 방법을 통해 적은 데이터를 가지고 점진적으로 사람의 행동과 표정을 학습하여 두 가지 결과의 조합을 통해 사람의 감성을 추측하는 알고리즘을 제안한다. 이 알고리즘을 통해 우리는 보다 종합적으로 사람의 감성을 파악할 수 있다.

Human emotions are revealed by various factors. Words, actions, facial expressions, attire and so on. But people know how to hide their feelings. So we can not easily guess its sensitivity using one factor. We decided to pay attention to behaviors and facial expressions in order to solve these problems. Behavior and facial expression can not be easily concealed without constant effort and training. In this paper, we propose an algorithm to estimate human emotion through combination of two results by gradually learning human behavior and facial expression with little data through the deep learning method. Through this algorithm, we can more comprehensively grasp human emotions.

키워드

참고문헌

  1. Xiaoming Zhao, Shiqing Zhang, "A Review on Facial Expression Recognition - Feature Extraction and Classification", IETE Technical Review, 2016. DOI : http://dx.doi.org/10.1080/02564602.2015.1117403
  2. H. Rowley, S. Baluja, and T. Kanade, "Human face detection in visual scenes." Technical Report CMU-CS-95-158R, School of Computer Science, Carnegie Mellon University, Nov. 1995.
  3. Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos, "A Facial Expression Recognition System Using Convolutional Networks", 28th SIBGRAPI Conference on Aug. 2015. DOI : http://dx.doi.org/10.1109/SIBGRAPI.2015.14
  4. Ken Chatfield, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman, "Return of the Devil in the Details: Delving Deep into Convolutional Nets", arXiv:1405.3531v4 [cs.CV] 5 Nov 2014.
  5. Karen Simonyan, Andrew Zisserman, "Very deep convolutional networks for large-scale image recognition", arXiv:1409.1556v6 [cs.CV] 10 Apr 2015.
  6. Cohn, D., Ghahramani, Z., Jordan, M., "Active learning with statistical models." Journal of Artificial Intelligence Research, Vol.4, pp. 129-145, 1999. DOI : https://doi.org/10.1613/jair.295
  7. Riccardi, G. and Hankkani-Tur, D., "Active learning: theory and applications to automatic speech recognition", IEEE Transactions on Speech and Audio Processing, Vol.13, No.4, pp. 504-511, 2005. DOI : https://doi.org/10.1109/TSA.2005.848882
  8. K. Simonyan, A. Vedaldi, and A. Zisserman, "Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps," Iclr, p. 1-, 2014.
  9. P. F. Felzenszwalb, R. B. Girshick, D. Mcallester, and D. Ramanan, "Object Detection with Discriminatively Trained Part-Based Models." Proceedings of the IEEE CVPR, 2008.
  10. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection.", Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.2005. DOI : http://dx.doi.org/0.1109/CVPR.2005.177 https://doi.org/10.1109/CVPR.2005.177
  11. I. Laptev, "On space-time interest points," in International Journal of Computer Vision, 2005. DOI : http://dx.doi.org/10.1007/s11263-005-1838-7
  12. C. Feichtenhofer, A. Pinz, and A. Zisserman, "Convolutional Two-Stream Network Fusion for Video Action Recognition," Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 1933-1941, 2016. arXiv:1604.06573 [cs.CV]
  13. G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik, "R-CNNs for Pose Estimation and Action Detection," arXiv Prepr. arXiv1406.5212, pp. 1-8, 2014. DOI : arXiv:1406.5212 [cs.CV]
  14. A. Richard, "A BoW-equivalent Recurrent Neural Network for Action Recognition Bag-of-Words Model as Neural Network," British Machine Vision Conference, 2015. DOI : https://dx.doi.org/10.1016/j.cviu.2016.10.014
  15. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770-778, 2016. arXiv:1512.03385 [cs.CV]
  16. B. Settles, "Active Learning," Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 6, no. 1, 2012. DOI : https://doi.org/10.2200/S00429ED1V01Y201207AIM018
  17. Alvin Poernomo, Dae-Ki Kang, "Content-Aware Convolutional Neural Network for Object Recognition Task.", IJASC, Vol. 5, No. 3 pp. 1-7 May 2016. DOI : https://doi.org/10.7236/IJASC.2016.5.3.1
  18. J.W.Kim, P.K.Rhee, "Active Semi-Supervised Learning, Facial Expression, Recognition, Deep Learning", The Journal of The Institute of Internet, Broadcasting and Communication(JIIBC), VOL. 17 NO. 2, pp.165-171, 2017. https://doi.org/10.7236/JIIBC.2017.17.2.165
  19. P.K.Rhee, Enkhbayar Erdenee, Shin Dong Kyun, Minhaz Uddin Ahmed, SongGuo Jin, "Active and semi-supervised learning for object detection with imperfect data" Cognitive Systems Research 45: 109-123 (2017) https://doi.org/10.1016/j.cogsys.2017.05.006