The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Optimization of Deep Learning Model Based on Genetic Algorithm for Facial Expression Recognition

얼굴 표정 인식을 위한 유전자 알고리즘 기반 심층학습 모델 최적화

  • Received : 2019.01.20
  • Accepted : 2020.02.15
  • Published : 2020.02.29
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Abstract

Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.

심층학습은 많은 양의 데이터셋을 학습에 활용하여 객체 분류, 검출, 분할 등의 영상 분석에 탁월한 성능을 나타내고 있다. 본 논문에서는 데이터셋의 종류가 다양한 얼굴 표정인식 데이터셋들을 활용하여 학습 데이터셋의 특성이 심층학습 성능에 영향을 줄 수 있음을 확인하고, 각 학습 데이터셋에 적합한 심층학습 모델의 구성 요소를 설정하는 방법을 제안한다. 제안하는 방법은 심층학습 모델의 성능에 영향을 주는 구성 요소인 활성함수, 그리고 최적화 알고리즘을 유전 알고리즘을 이용하여 선정한다. CK+, MMI, KDEF 데이터셋에 대해서 널리 활용되고 있는 심층학습 모델의 각 구성 요소별 다양한 알고리즘을 적용하여 성능을 비교 분석하고, 유전 알고리즘을 적용하여 최적의 구성 요소를 선정할 수 있음을 시뮬레이션을 통하여 확인한다.

Keywords

References

  1. B. Martinez, M. F. Valstar, B. Jiang, and M. Pantic, "Automatic analysis of facial actions: a survey," IEEE Trans. Affective Computing, vol. 10, no. 3, July 2017, pp. 325-347. https://doi.org/10.1109/taffc.2017.2731763
  2. H. Park, K. Kim, and E. Cha, "Facial Feature Extraction using Multiple Active Appearance Model," J. of Korean Institute of Electronic Communication Society, vol. 8, no. 8, Aug. 2013, pp. 1201-1206.
  3. Y. Kim, S. Park, and D. Kim, "Research on Robust Face Recognition against Lighting Variation using CNN," J. of Korean Institute of Electronic Communication Society, vol. 12, no. 2, Apr. 2017, pp. 325-330.
  4. P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, "The extended cohn-kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression," In Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops(CVPRW), San Francisco, USA, June 2010, pp. 13-18.
  5. M. Valstar and M. Pantic, "Induced disgust, happiness and surprise: an addition to the mmi facial expression database," In Proc. of Int. Conf. Language Resources and Evaluation, Workshop on EMOTION: Corpora for Research on Emotion and Affect, Valletta, Malta, May 2010, pp. 65-70.
  6. E. Goeleven, R. D. Raedt, L. Leyman, and B. Verschuere, "The Karolinska directed emotional faces: a validation study," J. of Cognition and Emotion, vol. 22. no. 6, Aug. 2008, pp. 1094-1118. https://doi.org/10.1080/02699930701626582
  7. W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone, Genetic Programming an Introduction. San Francisco: Morgan Kaufmann Publisher, 1955.
  8. L. M. Schmitt, C. L. Nehaniv, and R. H. Fujii, "Fundamental study linear analysis of genetic algorithms," Theoretical Computer Science, vol. 200, no. 1-2, June 1998, pp. 101-134. https://doi.org/10.1016/S0304-3975(98)00004-8
  9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep learning convolutional neural networks," In Proc. Neural Information Processing Systems(NIPS), Lake Tahoe, Nevada, USA, Dec. 2012, pp. 1097-1105.
  10. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going Deeper With Convolutions," In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, June 2015. pp. 1-9.
  11. X. Zhang, J. Zou, K. He, and J. Sun, "Accelerating Very Deep Convolutional Networks for Classification and Detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 38, no. 10, 2016, pp. 1943-1955. https://doi.org/10.1109/TPAMI.2015.2502579
  12. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," In Proc. IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, USA, June 2016, pp. 770-778.