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Authors: A. Drosou 1 ; K. Moustakas 2 ; D. Ioannidis 2 and D. Tzovaras 2

Affiliations: 1 Informatics and Telematics Institute;Imperial College London, United Kingdom ; 2 Informatics and Telematics Institute, Greece

ISBN: 978-989-674-028-3

Keyword(s): Biometric authentication, Biometrics, Activity recognition, Motion analysis, Body tracking, Hidden Markov models, HMM.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Feature Extraction ; Features Extraction ; Human-Computer Interaction ; Image and Video Analysis ; Image Filtering ; Image Formation and Preprocessing ; Implementation of Image and Video Processing Systems ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Physiological Computing Systems ; Signal Processing, Sensors, Systems Modeling and Control ; Tracking of People and Surveillance

Abstract: This paper proposes an innovative activity related authentication method for ambient intelligence environments, based on Hidden Markov Models (HMM). The biometric signature of the user is extracted, throughout the performance of a couple of common, every-day office activities. Specifically, the behavioral response of the user, stimuli related to an office scenario, such as the case of a phone conversation and the interaction with a keyboard panel is examined. The motion based, activity related, biometric features that correspond to the dynamic interaction with objects that exist in the surrounding environment are extracted in the enrollment phase and are used to train an HMM. The authentication potential of the proposed biometric features has been seen to be very high in the performed experiments. Moreover, the combination of the results of these two activities further increases the authentication rate. Extensive experiments carried out on the proprietary ACTIBIO-database verify this potential of activity related authentication within the proposed scheme. (More)

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Paper citation in several formats:
Drosou A.; Moustakas K.; Ioannidis D.; Tzovaras D. and (2010). ON THE POTENTIAL OF ACTIVITY RELATED RECOGNITION.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 340-348. DOI: 10.5220/0002832703400348

@conference{visapp10,
author={A. Drosou and K. Moustakas and D. Ioannidis and D. Tzovaras},
title={ON THE POTENTIAL OF ACTIVITY RELATED RECOGNITION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={340-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002832703400348},
isbn={978-989-674-028-3},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - ON THE POTENTIAL OF ACTIVITY RELATED RECOGNITION
SN - 978-989-674-028-3
AU - Drosou, A.
AU - Moustakas, K.
AU - Ioannidis, D.
AU - Tzovaras, D.
PY - 2010
SP - 340
EP - 348
DO - 10.5220/0002832703400348

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