Human emotions detection based on a smart-thermal system of thermographic images |
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| Affiliation: | 1. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India;2. CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Sector 30-C, Chandigarh 160030, India;3. Department of Neonatology, Government Medical College Hospital (GMCH), Chandigarh 160030, India;1. Human Performance Laboratory – LAPEH, University Federal of Viçosa, MG, Brazil;2. Facultad de Ciencias de la Actividad Física y del Deporte – INEF, Universidad Politécnica de Madrid, Spain;3. School Physical Education, University Federal de Minas Gerais, BH, MG, Brazil;1. Federal Institute for Education, Science and Technology of Minas Gerais, Campus Governador Valadares, Brasil;2. Extreme Environments Laboratory, Department of Sport and Exercise Science, University of Portsmouth, Portsmouth, UK;3. Department of Physical Education, Federal University of Juiz de Fora, Governador Valadares, Brazil;4. Józef Piłsudski University of Physical Education in Warsaw, Warsaw, Poland; Medical University of Warsaw, Warsaw, Poland;5. European Association of Thermology, Vienna, Austria; Medical Imaging Research Unit, University of South Wales, Pontypridd, United Kingdom;6. School of Exercise and Nutrition Sciences and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia;7. Aeronatutics Instruction and Adaptation Center, Minas Gerais, Brazil;8. Federal Institute for Education, Science and Technology of Minas Gerais, Campus Ipatinga, Brasil;9. School of Health and Sports Science, Cluster for Health Improvement, University of the Sunshine Coast, Australia;10. Department of Physical Therapy, Federal University of Paraíba, João Pessoa, Brazil;11. Department of Biomedical Sciences for Health, Università degli Studi di Milano, Italy;12. Thermal Sciences Laboratory, DECATHLON SportsLab, Villeneuve d''Ascq, France;13. Environmental Ergonomics Research Centre, Loughborough Design School, Loughborough University, Loughborough, United Kingdom;14. UCL Institute of Immunity and Transplantation, Royal Free Hospital, London, United Kingdom;15. Military Institute of Medicine, Warsaw, Poland;p. Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada;q. Autonomous University of San Luis Potosí, México;r. Department of Neurosciences, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Italy;s. Thermal Research Laboratory, Department of Kinesiology, Auburn University, Auburn, United States;t. Biophysics and Medical Physics Group, Department of Physiology, University of Valencia, Valencia, Spain;u. American Academy of Thermology. Piedmont Physical Medicine and Rehabilitation, PA, United States;v. Escola Superior de Saúde, Universidade Fernando Pessoa, Porto, Portugal;w. Manchester Metropolitan University, Manchester, United Kingdom;x. Rzhanov Institute of Semiconductor Physics SB RAS, Novosibirsk State University, Novosibirsk, Russia;y. Sports Department, Faculty of Sciences for Physical Activity and Sport (INEF), Technical University of Madrid, Madrid, Spain |
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| Abstract: | This work presents a noninvasive methodology to obtain biomedical thermal imaging which provide relevant information that may assist in the diagnosis of emotions. Biomedical thermal images of the facial expressions of 44 subjects were captured experiencing joy, disgust, anger, fear and sadness. The analysis of these thermograms was carried out through its thermal value not with its intensity value. Regions of interest were obtained through image processing techniques that allow to differentiate between the subject and the background, having only the subject, the centers of each region of interest were obtained in order to get the same region of the face for each subject. Through the thermal analysis a biomarker for each region of interest was obtained, these biomarkers can diagnose when an emotion takes place. Because each subject tends to react differently to the same stimuli, a self-calibration phase is proposed, its function is to have the same thermal trend for each subject in order to make a decision so that the five emotions can be correctly diagnosed through a top-down hierarchical classifier. As a final result, a smart-thermal system that diagnose emotions was obtained and it was tested on twenty-five subjects (625 thermograms). The results of this test were 89.9% successful. |
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| Keywords: | Infrared thermography Emotion Region of Interest Diagnostic Self-calibration and biomarker |
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