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Study on facial thermal reactions for psycho-physical stimuli

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a study on the influence of psychophysical stimuli on facial thermal emissions. Two independent groups of stimuli are proposed to investigate facial changes resulting from human stress and physical exhaustion. One pertains to physical effort while the other is linked to stress invoked by solving a simple written test. Subjects’ face reactions were measured through collecting and analyzing long-wavelength infrared images. A methodology for numerical processing of images is proposed. Results of numerical analysis with respect to different facial regions of interest are provided. An automatic deep learning based algorithm to classify specific thermal face patterns is proposed. The algorithm consists of detection of regions of interests as well as numerical analysis of thermal energy emissions of facial parts. The results of presented experiments allowed the authors to associate emission changes in specific facial regions with psychophysical stimulations of the person being examined. This work proves high usability of thermal imaging to capture changes of heat distribution of face as reactions for external stimuli.
Rocznik
Strony
399--415
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr., wzory
Twórcy
  • Military University of Technology, Faculty of Mechatronics and Aerospace, gen. Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
  • Military University of Technology, Faculty of Mechatronics and Aerospace, gen. Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
  • Military University of Technology, Institute of Optoelectronics, gen. Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
  • Military University of Technology, Institute of Optoelectronics, gen. Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
Bibliografia
  • [1] Terelak, J. (2001). Psychologia stresu. Bydgoszcz: BRANTA.
  • [2] Adelson, R. (2004). Detecting deception. APA Monitor on Psychology, 35(7), 70-73.
  • [3] Gołaszewski, M., Zając, P., Widacki, J. (2015). Thermal Vision as a Method of Detection of Deception: A Review of experiences. European Polygraph, 9(1), 5-24.
  • [4] Powar, N.U., Schneider, T.R., Skipper, J.A., Petkie, D.T., Asari, V.K., Riffle, R.R., Sherwood, M.S. (2017). Thermal Facial Signatures for State Assessment during Deception. Proc. of the IS&T International Symposium on Electronic Imaging Science and Technology, USA, 95-104.
  • [5] Or, C.K.L., Duffy, V.G. (2007). Development of a facial skin temperature-based methodology for non-intrusive mental workload measurement. Occupational Ergonomics, 7(2), 83-94.
  • [6] Ghahramani, A., Castro, G., Becerik-Gerber, B., Yu, X. (2016). Infrared thermography of human facefor monitoring thermoregulation performance and estimating personal thermal comfort. Building and Environment, 109, 1-11.
  • [7] Khan, M.M., Ingleby, M., Ward, R.D. (2006). Automated facial expression classification and affect interpretation using infrared measurement of facial skin temperature variation. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 1(1), 91-113.
  • [8] Nhan, B.R., Chau, T. (2010). Classifying affective states using thermal infrared imaging of the human face. IEEE Transactions on Biomedical Engineering, 57(4), 979-987.
  • [9] Khan, M.M. (2008). Cluster-analytic classification of facial expressions using infrared measurements facial thermal features. [Doctoral Dissertation, University of Huddersfield]. http://eprints.hud.ac.uk/id/eprint/732/ (accessed on Aug. 2020).
  • [10] Oz, I.A., Khan, M.M. (2012). Efficacy of biophysiological measurements at FTFPs for facial expression classification: A validation. Proc. of the IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, Hong Kong, China, 108-111.
  • [11] Marzec, M., Koprowski, R., Wróbel, Z., Kleszcz, A., Wilczyński, S. (2015). Automatic method for detection of characteristic areas in thermal face images. Multimedia Tools and Applications, 74(12), 4351-4368.
  • [12] Marzec, M., Koprowski, R., Wróbel, Z. (2009). Determination of the characteristic face regions on thermograms. Biomedical Engineering, 15, 2007-2010.
  • [13] Jones, B.F. (1998). A re-appraisal of the use of infrared thermal image analysis in medicine. IEEE Trans. Medical Imaging. 17(6), 1019-1027.
  • [14] Jones, B.F., Plassmann, P. (2002). Digital infrared thermal imaging of human skin. IEEE Engineering in Medicine and Biology Magazine, 21(6), 41-48.
  • [15] Tanda, G. (2016). Skin temperature measurements by infrared thermography during running exercise. Experimental Thermal and Fluid Science, 71, 103-113.
  • [16] Smith, C.J., Havenith, G. (2011). Body mapping of sweating patterns in male athletes in mild exercise-induced hyperthermia. European Journal of Applied Physiology, 111(7), 1391-1404.
  • [17] Cruz-Albarran, I.A., Benitez-Rangel, J.P., Osornio-Rios, R.A., Morales-Hernandez, L.A. (2017). Human emotions detection based on a smart-thermal system of thermographic images. Infrared Physics& Technology, 81, 250-261.
  • [18] Warmelink, L., Vrij, A., Mann, S., Leal, S., Forrester, D., Fisher, R.P. (2001). Thermal imaging as a lie detection tool at airports. Law and Human Behavior, 35(1), 40-48.
  • [19] Pavlidis, I., Levine, J., Baukol, P. (2001). Thermal Imaging for Anxiety Detection. Proc. of the IEEE International Conference on Image Processing (ICIP), Thessaloniki, Greece, 2, 315-318.
  • [20] Ren S., He K., Girshick R., Sun J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149.
  • [21] Viola, P., Jones, M. J. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision, 57(2), 137-154.
  • [22] Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2006). You Only Look Once: Unified, Real-Time Object Detection. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 779-788.
  • [23] Kowalski, M., Grudzień, A. (2018). High-resolution thermal face dataset for face and expression recognition. Metrology and Measurement Systems, 25(2), 403-415.
  • [24] Sequeira A.F., et al. (2018). PROTECT Multimodal DB: a multimodal biometrics dataset envisaging Border Control. Prof. of the International Conference of the Biometrics Special Interest Group (BIOSIG), Germany.
  • [25] Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems, 25.
  • [26] Simonyan, K., Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proc. of the 3rd International Conference on Learning Representations, San Diego, USA.
  • [27] Szegedy, Ch., et al. (2015). Going Deeper with Convolutions. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA.
  • [28] He, K., Zhang, X, Ren, S., Sun, J. (2016). Deep Residual Learning for Image Recognition. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9b101f70-3d82-41cd-842e-9e9e5e7cd37c
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