Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation
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 arou...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
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IEEE Engineering in Medicine and Biology Society
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/37262 |
| _version_ | 1848754998310076416 |
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| author | Khan, Masood Mehmood Oz, Ihsan |
| author2 | Yuan-Ting Zhang |
| author_facet | Yuan-Ting Zhang Khan, Masood Mehmood Oz, Ihsan |
| author_sort | Khan, Masood Mehmood |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:49:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-37262 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:49:19Z |
| publishDate | 2012 |
| publisher | IEEE Engineering in Medicine and Biology Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-372622017-01-30T14:01:11Z Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation Khan, Masood Mehmood Oz, Ihsan Yuan-Ting Zhang biophysiological signal processing Affective computing affect recognition facial expression classification 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. 2012 Conference Paper http://hdl.handle.net/20.500.11937/37262 IEEE Engineering in Medicine and Biology Society fulltext |
| spellingShingle | biophysiological signal processing Affective computing affect recognition facial expression classification Khan, Masood Mehmood Oz, Ihsan Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation |
| title | Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation |
| title_full | Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation |
| title_fullStr | Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation |
| title_full_unstemmed | Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation |
| title_short | Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation |
| title_sort | efficacy of biophysiological measurements at ftfps for facial expression classification: a validation |
| topic | biophysiological signal processing Affective computing affect recognition facial expression classification |
| url | http://hdl.handle.net/20.500.11937/37262 |