Thermal spatio-temporal data for stress recognition

Nandita Sharma, Abhinav Dhall, Tom Gedeon, Roland Goecke

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

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

Original languageEnglish
Article number28
Pages (from-to)1-12
Number of pages12
JournalEurasip Journal on Image and Video Processing
Volume2014
Issue number1
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Genetic algorithms
  • Stress classification
  • Support vector machines
  • Temporal stress
  • Thermal imaging
  • Watching films

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