Derakhshan, amin, Khalilzadeh, mohammad ali, Mohammadian, amin (2011). Anxiety Detection by Periorbital Mean Temperature Signal Analysis. , 10(4), 101-113.
amin Derakhshan; mohammad ali Khalilzadeh; amin Mohammadian. "Anxiety Detection by Periorbital Mean Temperature Signal Analysis". , 10, 4, 2011, 101-113.
Derakhshan, amin, Khalilzadeh, mohammad ali, Mohammadian, amin (2011). 'Anxiety Detection by Periorbital Mean Temperature Signal Analysis', , 10(4), pp. 101-113.
Derakhshan, amin, Khalilzadeh, mohammad ali, Mohammadian, amin Anxiety Detection by Periorbital Mean Temperature Signal Analysis. , 2011; 10(4): 101-113.

Anxiety Detection by Periorbital Mean Temperature Signal Analysis

The Modares Journal of Electrical Engineering
Article 7, Volume 10, Issue 4, 2011, Pages 101-113    PDF (840.12 K)
Authors
amin Derakhshan* 1; mohammad ali Khalilzadeh2; amin Mohammadian3
1Imamreza International University, Mashhad
2Islamic Azad University, Mashhad Branch
3Research Center of Intelligent Signal Processing
Abstract
It has been demonstrated that anxiety is accompanied by significant warm up in the periorbital area. This warm up was attributed to the increased blood circulation in the area around the eyes. The whole pattern makes physiological and evolutionary sense since it represents a mechanism to facilitate rapid eye movements during preparedness for fight. This increased blood flow dissipates convective heat, which can be monitored through thermal imaging. The evolution of these variables along the timeline and across the 2D space can reveal important clues about anxiety. In this work, we use both facial thermal imaging analysis and poly graph examination to evaluate the effectiveness of thermal imaging in situation of anxiety. The system had been evaluated on six subjects and for each of them four times, each time in two minutes. It operates on the raw temperature signal and tries to improve the information content by suppressing the noise level instead of amplifying the signal as a whole. Finally, a pattern recognition method classifies stressful (deceptive) from non-stressful (non-deceptive) subjects based on a comparative measure between the entire baseline signal and a transient response. The successful classification rate with Multi Layer Perceptron is about 74.7% that is a little better than the LDA method.
Keywords
Anxiety; Thermal Imaging; Ploy Graph; Multi-Layer Perceptron(MLP)
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