Pusat Pengajian Sains Komputer - Tesis

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Analysis Of Feature Reduction Algorithms To Estimate Human Stress Conditions
(2022-09)
Arasu, Darshan Babu
Stress is a normal reaction of the human organism which triggered in situations that require a certain level of activation. This reaction has both positive and negative effects on everyone’s life. Thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner. Therefore, this study aimed to present analyse of the performance of feature classify when combining with feature selection algorithm to estimate human stress based on the facial feature of thermal imaging. Three hybrid classifiers, Support Vector Machine (SVM), Decision Tree (DT) and Logistic Regression (LR) combined with feature reduction analysis, Principal Component Analyse (PCA) and Analysis of Variance (ANOVA) was evaluated with 10-fold validation to compute classification accuracy. Four statistical features was extracted; mean, maximum, minimum and standard deviation of the gray scale value from six area regions of interest. Results showing that hybrid classifier DT-ANOVA achieves higher accuracy of 62% compared to others 90 combination classifiers. The findings demonstrated that DT-ANOVA performs well with a small dataset, while SVM and LR can improve the accuracy when fused with ANOVA for a big dataset. The findings also suggested that ANOVA can provides comparable performance as PCA.