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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Eckman, P.: Telling lies, 2nd edn. Norton (2009)
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)
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)
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)
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)
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)
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)
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)
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)
Zhai, J.: Stress detection in computer users through non-invasive monitoring of physiological signals. Biomed. Sci. Instrum. 45, 495–500 (2006)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Polikovsky, S., Quiros-Ramirez, M.A., Onisawa, T., Kameda, Y., Ohta, Y. (2013). A Non-invasive Multi-sensor Capturing System for Human Physiological and Behavioral Responses Analysis. In: Schwenker, F., Scherer, S., Morency, LP. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2012. Lecture Notes in Computer Science(), vol 7742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37081-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-37081-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37080-9
Online ISBN: 978-3-642-37081-6
eBook Packages: Computer ScienceComputer Science (R0)