A Non-invasive Multi-sensor Capturing System for Human Physiological and Behavioral Responses Analysis
Conference paper
Abstract
We present a new noninvasive multi-sensor capturing system for recording video, sound and motion data. The characteristic of the system is its 1msec. order accuracy hardware level synchronization among all the sensors as well as automatic extraction of variety of ground truth from the data. The proposed system enables the analysis of the correlation between variety of psychophysiological model (modalities), such as facial expression, body temperature changes, gaze analysis etc... . Following benchmarks driven framework principles, the data captured by our system is used to establish benchmarks for evaluation of the algorithms involved in the automatic emotions recognition process.
Keywords
sensor-fusion synchronization benchmarksPreview
Unable to display preview. Download preview PDF.
References
- 1.Eckman, P.: Telling lies, 2nd edn. Norton (2009)Google Scholar
- 2.Polikovsky, S., Quiros-Ramirez, M.A., Kameda, Y., Burgoon, J., Ohta, Y.: Benchmark Driven Framework for Development of Emotion Sensing Support Systems. In: Workshop on Innovation in Border Control (2012)Google Scholar
- 3.Jensen, M.L., Meservy, T.O., Burgoon, J.K., Nunamaker, J.F.: Video-based deception detection. In: Intelligence and Security Informatics, pp. 425–441 (2008)Google Scholar
- 4.Polikovsky, S., Kameda, Y., Ohta, Y.: Evaluation of synchronization accuracy between high speed cameras in infrared and visible spectrums. In: 3rd International Conference on Imaging for Crime Detection and Prevention (2009)Google Scholar
- 5.Lichtenauer, J., Shen, J., Valstar, M.F., Pantic, M.: Cost-effective solution to synchronised audio-visual data capture using multiple sensors. Image and Vision Computing 29, 666–680 (2011)CrossRefGoogle Scholar
- 6.Pavlidis, I.T., Frank, M.G., Ekman, P.: Imaging facial physiology for the detection of deceit. International Journal of Computer Vision 71(2), 197–214 (2001)Google Scholar
- 7.Calvo, R., Mello, S.D.: Affect detection: an interdisciplinary review of models, methods and their applications. IEEE Transactions on Affective Computing 1, 18–37 (2010)CrossRefGoogle Scholar
- 8.Ruiz, R., Legros, C., Guell, A.: Voice analysis to predict the psychological or physical state of a speaker. Aviation Space and Environmental Medicine 61(3), 266–271 (1990)Google Scholar
- 9.Polikovsky, S.: Evaluation of synchronization accuracy between high speed cameras in infrared and visible spectrums. In: Proceedings of IAPR Conference on Machine Vision Applications, pp. 51–54 (2010)Google Scholar
- 10.Zhai, J.: Stress detection in computer users through non-invasive monitoring of physiological signals. Biomed. Sci. Instrum. 45, 495–500 (2006)Google Scholar
- 11.Quiros-Ramirez, M.A., Polikovsky, S., Kameda, Y., Onisawa, T.: Towards Developing Robust Multimodal Databases for Emotion Analysis. In: Proc. of 6th SCIS-ISIS (2012)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2013