Theodoros Anagnostopoulos
ABSTRACT
Focusing on the research results of psychologists and epistemologists it is an open issue whether people who commit suicide do so as a result of free will or if they suffer from chronic or occasional depression. During the decision making process of a potential suicide the facial features, the voice frequencies and the body movement gestures express the depressive emotional state of a specific individual. In metro stations and other public space there are surveillance systems which can capture facial expressions, body movement and speech recognition of an individual. In this paper it is proposed a design of an information system architecture which can predict whether an individual intends to commit a suicide in an urban metro station or not, in a critical period of time.
- Matthews, G., Zeidner, M., and Roberts, R. D. 2004. Emotional Intelligence Science and Myth, Cambridge, Massachusetts, The MIT Press.Google Scholar
- Schacter, D. L. 2011. Psychology Second Edition, 41 Madison Avenue, New York, NY 10010, Worth Publishers.Google Scholar
- Gaulin, S. J. C., and Mc Burney, D. H. 2003. Evolutionary Psychology, Prentice Hall.Google Scholar
- Thompson, E. R. 2007. Development and validation of an internationally reliable short-form of the positive and negative affect schedule (PANAS), Journal of Cross-Cultural Psychology, 38(2), 227--242.Google ScholarCross Ref
- Parkinson, B., and Gweda, S. 2012. Worry spreads: Interpersonal transfer of problem-related anxiety, Cognition & Emotion, 26 (3), 462--479.Google ScholarCross Ref
- Picard, R. W. 2000. Affective Computing, Cambridge, Massachusetts, The MIT Press. Google ScholarDigital Library
- Goleman, D., 1995. Emotional Intelligence, New York, NY, England, Bantam Books, Inc.Google Scholar
- Walsh, B. C., and Farrington, D. P. 2009. Public area CCTV and crime prevention: An updated systematic review and meta-analysis, Justice Quarterly, 26(4), 716--745.Google ScholarCross Ref
- Bettadapura, V. 2012. Face Expression Recognition and Analysis: The State of the Art, Technical Report, arXiv:1203.6722, Georgia Tech University.Google Scholar
- Cohn, J. F., et al. 2009. Detecting Depression from Facial Actions and Vocal Prosody, 3rd IEEE International Conference on Affective Computing and Intelligent Interaction and Workshops, Amsterdam, 1--7.Google ScholarCross Ref
- Meng, H., et al. 2011. Emotion recognition by two view SVM_2K classifier on dynamic facial expression features, 2011 IEEE Internation Conference on Automatic Face and Gesture Recognition and Workshops, Santa Barbara, CA, 854--859.Google ScholarCross Ref
- Nagaraju, C., Srinu, B., and Rao E. S. 2013. An efficient Facial Features extraction Technique for Face Recognition system Using Local Binary Patterns. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2(6).Google Scholar
- Mitra, S., and Acharya, T. 2007. Gesture Recognition: A Survey, IEEE Transactions on Systems, MAN, and Cybernetics, 37(3). Google ScholarDigital Library
- Sharma, A., et al. 2002. Representation and Classification of Human Movement Using Temporal Templates and Statistical Measure of Similarity, Workshop on the Internet, Telecommunications and Signal Processing, Wollongong, NSW, Australia.Google Scholar
- Aggarwal, J. K., and Cai, Q. 1999, Human Motion Analysis: A Review. Computer Vision and Image Understanding, 73(3). 428--440. Google ScholarDigital Library
- Wu, Y., and Huang, T. S. 1999. Vision-Based Gesture Recognition: A Review, GW '99, Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction, London, UK, 103--115. Google ScholarDigital Library
- Anusuya M. A., and Katti, S. K. 2009. Speech Recognition by Machine: A Review. (IJCSIS) International Journal of Computer Science and Information Security, 6(3).Google Scholar
- Cho, Y. H., Park, K. S., and Pak R. J. 2007. Speech Emotion Pattern Recognition Agent in Mobile Communication Environment Using Fuzzy-SVM, Fuzzy Information and Engineering (ICFIE), ASC 40, 419--430.Google Scholar
- Rong, J. et al. 2007. Acoustic Features Extraction for Emotion Recognition, 6th IEEE/ACIS International Conference on Computer and Information Science, (ICIS 2007).Google ScholarCross Ref
- Yang, N. et al. 2012. Speech-Based Emotion Classification using Multiclass SVM with Hybrid Kernel and Thresholding Fusion, 2012 IEEE Spoken Language Technology Workshop (SLT), Miami, Fl, 455--460.Google Scholar
Index Terms
- A Surveillance System for Preventing Suicide Attempts in Urban Metro Stations
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