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Internal state measurement from facial stereo thermal and visible sensors through SVM classification

Mohd, Mohd Norzali and Kashima, Masayuki and Sato, Kiminori and Watanabe, Mutsumi (2015) Internal state measurement from facial stereo thermal and visible sensors through SVM classification. In: International Conference on Electrical and Electronic Engineering 2015 (IC3E 2015), 10-11 August 2015 , Melaka, Malaysia.

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Abstract

Our main aim is to propose a vision-based measurement as an alternative to physiological measurement for recognizing mental stress. The development of this emotion recognition system involved three stages: experimental setup for vision and physiological sensing, facial feature extraction in visual-thermal domain, mental stress stimulus experiment and data analysis and classification based on Support Vector Machine. In this research, 3 vision-based measurement and 2 physiological measurement were implemented in the system. Vision based measurement in facial vision domain consists of eyes blinking and in facial thermal domain consists 3 ROI`s temperature value and blood vessel volume at Supraorbital area. Two physiological measurement were done to measure the ground true value which is heart rate and salivary amylase level. We also propose a new calibration chessboard attach with fever plaster to locate calibration point in stereo view. A new method of integration of two different sensors for detecting facial feature in both thermal and visual is also presented by applying nostril mask, which allows one to find facial feature namely nose area in thermal and visual domain. Extraction of thermal-visual feature images was done by using SIFT feature detector and extractor to verify the method of using nostril mask. Based on the experiment conducted, 88.6% of correct matching was detected. In the eyes blinking experiment, almost 98% match was detected successfully for without glasses and 89\% with glasses. Graph cut algorithm was applied to remove unwanted ROI. The recognition rate of 3 ROI`s was about 90%-96%. We also presented new method of automatic detection of blood vessel volume at Supraorbital monitored by LWIR camera. The recognition rate of correctly detected pixel was about 93%. An experiment to measure mental stress by using the proposed system based on Support Vector Machine classification had been proposed and conducted and showed promising results.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: thermal face image analysis; facial feature extraction; thermal-infrared geometric camera calibration; blood vessel extraction in thermal infrared
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 29 Oct 2015 06:23
Last Modified: 29 Oct 2015 06:23
URI: http://eprints.uthm.edu.my/id/eprint/7150
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