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Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation

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Open access
Authors
Khan, Masood Mehmood
Oz, Ihsan
Date
2012
Type
Conference Paper

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Khan, Masood Mehmood and A. Oz, Ihsan. 2012. Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation, in Yuan-Ting Zhang (ed), IEEE-EMBS 2012-BHI, International Conference on Biomedical and Health Informatics, Jan 5 2012, pp. 108-111. Shenzhen, China: IEEE Engineering in Medicine and Biology Society
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Proceedings of IEEE-EMBS 2012-BHI, International Conference on Biomedical and Health Informatics
Source Conference
IEEE-EMBS 2012-BHI, International Conference on Biomedical and Health Informatics
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Department of Mechanical Engineering
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© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Abstract

Recent works suggest that thermal intensity values (TIVs) measured around the facial thermal feature points (FTFPs) can help in distinguishing between the facial expression of affective states. This work investigates if the average pixel grey-levels, instead of TIVs, measured in sub-image masks around the FTFPs allow classifying facial expressions. Thermal infrared images from the IEEE OTCBVS database were used to distinguish between facial expressions. The pixel grey-levels measured in sub-image masks were used to measure, for each individual, the Euclidean distance between images of different facial expressions. Linear discriminant analysis was performed to obtain hyper-planes for separating the clusters of sample images. Significant pixel grey-level differences were observed at FTFPs between three facial expressions; neutral, happy, and angry. More than 96 of the original images in a three-expression Gaussian mixture model were separable and clustered around distant centroids in a discriminant space.