In this paper, we propose a classification model for learning state based on individual biometric data. In particular, we use the pupil size as a biometric data and the data has been collected from 72 participants. We also deploy the support vector machine (SVM) in conjunction with k-fold validation as an analysis tool. In order to improve the performance of the SVM, the we remove outliers from the data set and normalize it. Our experiment results show that the accuracy of the proposed classification model is up to 68.8% and thus confirm the effectiveness of the proposed classification model using the pupil data.
|Title of host publication||International Conference on Information and Communication Technology Convergence|
|Subtitle of host publication||ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||3|
|State||Published - 12 Dec 2017|
|Event||8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of|
Duration: 18 Oct 2017 → 20 Oct 2017
|Name||International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017|
|Conference||8th International Conference on Information and Communication Technology Convergence, ICTC 2017|
|Country/Territory||Korea, Republic of|
|Period||18/10/17 → 20/10/17|
Bibliographical noteFunding Information:
This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2B4005041), in part by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5B6036244) and in part by the MSIT, under the ITRC (Information Technology Research Center) support program (IITP-2017-2012-0-00559) supervised by the IITP (Institute for Information & communications Technology Promotion).
© 2017 IEEE.
- Support Vector Machine
- classification model
- k-fold cross validation
- machine learning