Stress is a serious concern facing our world today, motivating the development of a better objective understanding\nthrough the use of non-intrusive means for stress recognition by reducing restrictions to natural human behavior.\nAs an initial step in computer vision-based stress detection, this paper proposes a temporal thermal spectrum (TS)\nand visible spectrum (VS) video database ANUStressDB - a major contribution to stress research. The database\ncontains videos of 35 subjects watching stressed and not-stressed film clips validated by the subjects. We present\nthe experiment and the process conducted to acquire videos of subjects' faces while they watched the films for\nthe ANUStressDB. Further, a baseline model based on computing local binary patterns on three orthogonal planes\n(LBP-TOP) descriptor on VS and TS videos for stress detection is presented. A LBP-TOP-inspired descriptor was used\nto capture dynamic thermal patterns in histograms (HDTP) which exploited spatio-temporal characteristics in TS\nvideos. Support vector machines were used for our stress detection model. A genetic algorithm was used to select\nsalient facial block divisions for stress classification and to determine whether certain regions of the face of subjects\nshowed better stress patterns. Results showed that a fusion of facial patterns from VS and TS videos produced\nstatistically significantly better stress recognition rates than patterns from VS or TS videos used in isolation.\nMoreover, the genetic algorithm selection method led to statistically significantly better stress detection rates than\nclassifiers that used all the facial block divisions. In addition, the best stress recognition rate was obtained from\nHDTP features fused with LBP-TOP features for TS and VS videos using a hybrid of a genetic algorithm and a\nsupport vector machine stress detection model. The model produced an accuracy of 86%.
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