Multiple Classifier Learning of New Facial Extraction Approach for Facial Expressions Recognition using Depth Sensor

Nattawat Chanthaphan, Keiichi Uchimura, Takami Satonaka, Tsuyoshi Makioka

2016

Abstract

In this paper, we are justifying the next step experiment of our novel feature extraction approach for facial expressions recognition. In our previous work, we proposed extracting the facial features from 3D facial wire-frame generated by depth camera (Kinect V.2). We introduced the facial movement streams, which were derived from the distance measurement between each pair of the nodes located on human facial wire-frame flowing through each frame of the movement. The experiment was conducted by using two classifiers, K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM), with fixed values of k parameter and kernel. 15-people data set collected by our software was used for the evaluation of the system. The experiment resulted promising accuracy and performance of our approach in the last experiment. Consequently, we were anticipating to know the best parameters that would reflect the best performance of our approach. This time experiment, we try tuning the parameter values of K-NN as well as kernel of SVM. We measure both accuracy and execution time. On the one hand, K-NN overcomes all other classifiers by getting 90.33% of accuracy, but on the other hand, SVM consumes much time and gets just 67% of accuracy.

References

  1. Chanthaphan, N., Uchimura, K., Satonaka, T., Makioka, T., 2015. Facial emotion recognition based on facial motion stream generated by Kinect. In 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). pp. 117-124.
  2. Chanthaphan, N., Uchimura, K., Satonaka, T., Makioka, T., 2016. New feature extraction method for facial emotion recognition by using Kinect. In The KoreaJapan joint workshop on Frontiers of Computer Vision (FCV). pp. 200-205.
  3. Cootes, T. F., Taylor, C. J., 1992. Active shape models - Smart snakes. In Proc. British Machine Vision Conference (BMVC). pp. 266-275.
  4. Cootes, T. F., Edwards, G. J., Taylor, C. J., 1998. Active appearance models. In 5th European Conference on Computer Vision (ECCV). vol. 2, no. 1, pp. 484-498.
  5. Cristinacce, D., Cootes, T. F., 2006. Feature detection and tracking with constrained local models. In Proc. British Machine Vision Conference (BMVC). vol. 3, no. 1, pp. 929-938.
  6. Blanz, V., Vetter, T., 1999. A morphable model for the synthesis of 3D faces. In Proc. the 26th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 7899). pp. 187-194.
  7. Baltrusaitis, T., Robinson, P., Morency, LP., 2012. 3D constrained local model for rigid and non-rigid facial tracking. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2610- 2617.
  8. Zhao, X., Li, X., Pang, C., Sheng, Q. Z., Wang, S., Ye, M., 2014. Structured streaming skeleton - a new feature for online human gesture recognition. In ACM Trans. Multimedia Comput. Commun. Appl.. vol. 11, no. 1, pp. 1-18.
  9. Baggio, D. L., Emami, S., Escrivá, D. M., Ievgen, K., Mahmood, N., Saragih, J., Shilkrot, R., 2012. Mastering OpenCV with practical computer vision projects. Packt Publishing Ltd. Birmingham. pp. 235- 260.
  10. Dementhon, D. F., Davis, L. S., 1995. Model-based object pose in 25 lines of code, In International Journal of Computer Vision. vol. 15, no. 1-2, pp. 123-141.
  11. Lawson, C. L., 1972. Transforming triangulations, In Discrete Mathematics. vol. 3, no. 1, pp. 365-372.
  12. Zhu, X., Ramanan, D., 2012. Face detection, pose estimation, and land mark localization in the wild, In Proc. IEEE Computer Vision and Pattern Recognition (CVPR). pp. 2879-2886.
  13. Zhang, Z., 2012. Microsoft Kinect sensor and its effect. In IEEE Computer Society. vol. 19, no. 2, pp. 4-12.
  14. Piana, S., Staglianò, A., Odone, F., Verri, A., Camurri, A., 2014. Real-time automatic emotion recognition from body gestures. Cornell university library: Computing Research Repository (CoRR), pp. 1-7.
  15. Mao, Q. R., Pan, X. Y., Zhan, Y. Z., Shen, X. J., 2015. Using Kinect for real-time emotion recognition via facial expressions. In Frontiers of Information Technology & Electronic Engineering, vol. 16, no. 4, pp. 272-282.
  16. Ekman, P., Friesen, W., 1976. Measuring facial movement. In Environmental psychology and nonverbal behaviour. pp. 56-75.
  17. Sakoe, H., Chiba, S., 1978. Dynamic programming algorithm optimization for spoken word recognition. In IEEE Trans. on Acoustics, Speech and Signal Processing. vol. 26, no. 1, pp. 43-49.
  18. Sakurai, Y., Faloutsos, C., Yamamuro, M., 2007. Stream monitoring under the time warping distance. In Proc. IEEE International Conference on Data Engineering (ICDE). pp. 1046-1055.
  19. Yang, Z., Zhu, Y., Pu, Y., 2008. Parallel Image Processing Based on CUDA. In Computer Science and Software Engineering. vol. 3, no. 1, pp.198-201.
  20. Jovic, A., Brkic, K., Bogunovic, N., 2014. An overview of free software tools for general data mining. In Proc. Information and Communication Technology, Electronics and Microelectronics (MIPRO). pp. 1112- 1117.
Download


Paper Citation


in Harvard Style

Chanthaphan N., Uchimura K., Satonaka T. and Makioka T. (2016). Multiple Classifier Learning of New Facial Extraction Approach for Facial Expressions Recognition using Depth Sensor . In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 5: SIGMAP, (ICETE 2016) ISBN 978-989-758-196-0, pages 19-27. DOI: 10.5220/0005948000190027


in Bibtex Style

@conference{sigmap16,
author={Nattawat Chanthaphan and Keiichi Uchimura and Takami Satonaka and Tsuyoshi Makioka},
title={Multiple Classifier Learning of New Facial Extraction Approach for Facial Expressions Recognition using Depth Sensor},
booktitle={Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 5: SIGMAP, (ICETE 2016)},
year={2016},
pages={19-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005948000190027},
isbn={978-989-758-196-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 5: SIGMAP, (ICETE 2016)
TI - Multiple Classifier Learning of New Facial Extraction Approach for Facial Expressions Recognition using Depth Sensor
SN - 978-989-758-196-0
AU - Chanthaphan N.
AU - Uchimura K.
AU - Satonaka T.
AU - Makioka T.
PY - 2016
SP - 19
EP - 27
DO - 10.5220/0005948000190027