Texture descriptors based affective states recognition- frontal face thermal image

• Main Authors: Latif, M. Hafiz, Md Yusof, Hazlina, Sidek, Shahrul Na'im, Rusli, Nazreen
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2016
• Subjects: T61 Technical education. Technical schools
• Online Access: 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; http://irep.iium.edu.my/59674/; http://ieeexplore.ieee.org/abstract/document/7843419/

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.