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

Nattawat Chanthaphan, Keiichi Uchimura, Takami Satonaka, Tsuyoshi Makioka

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

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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