Texture descriptors based affective states recognition- frontal face thermal image
Recognition of human affective states could be achieved through affective computing via various modalities; speech, facial expression, body language, physiological signals etc. In this paper, we present a noninvasive approach for affective states recognition based on frontal face (periorbital, supra...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/59674/ http://irep.iium.edu.my/59674/ http://irep.iium.edu.my/59674/ http://irep.iium.edu.my/59674/1/59674_Texture%20Descriptors%20Based%20Affective%20States.pdf http://irep.iium.edu.my/59674/2/59674_Texture%20Descriptors%20Based%20Affective%20States_SCOPUS.pdf |
| Summary: | Recognition of human affective states could be achieved through affective computing via various modalities; speech, facial expression, body language, physiological signals etc. In this paper, we present a noninvasive approach for affective states recognition based on frontal face (periorbital, supraorbital, maxillary/nose and mouth region) thermal images. The GLCM features derived from the PCA of the four level decomposition of 2D-DWT (Daubechies-4 Mother wavelet) and LBP features are exploited to provide useful information related to the affective states. The mean classification accuracy of 98.6% was achieved (SVM-Gaussian kernel). The findings of this study endorse the earlier findings; thermal imaging ability to quantify Autonomous Nervous System (ANS) parameters through contactless, nonintrusive and noninvasive manner for affect detection. |
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