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Authors: Timon Bloecher 1 ; Leyre Garralda Iriarte 2 ; Johannes Schneider 1 ; Christoph Zimmermann 1 and Wilhelm Stork 1

Affiliations: 1 FZI Forschungszentrum Informatik, Germany ; 2 Public University of Navarre, Spain

ISBN: 978-989-758-212-7

Keyword(s): Anti-spoofing, Biometrics, Presentation Attack Detection, Blinking Rate, Face Detection, Face Tracking, Face Liveness Detection, Life Signs, Time Series Analysis, Photoplethysmography Imaging (PPGI).

Related Ontology Subjects/Areas/Topics: Applications ; Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Detection and Identification ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Multimedia ; Multimedia Signal Processing ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Telecommunications

Abstract: The use of image based presentation attack detection (PAD) systems has experienced an enormous growth of interest in recent years. The most accurate techniques in literature addressing this topic rely on the verification of the actual three-dimensionality of the face, which increases complexity and costs of the system. In this work, we propose an effective and low-cost face spoofing detector system to supplement a PPGI-based vital signal monitoring application. Starting from a set of automatically located facial feature points, the movement information of this set of points was obtained. Based on a time series analysis of the landmark position distances using peak descriptors and cross-correlation coefficients as classifiers in a sliding window, life signs have been exploited to develop a system being able to recognize false detections and biometric spoofs. To verify the performance, experiments on three different benchmark datasets (CASIA face anti-spoofing, MSU and IDIAP Re play-Attack databases) were made. The evaluation of the proposed low-cost approach showed good results (accuracy of ~85-95%) compared to more resource-intensive state-of-the-art methods. (More)

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Paper citation in several formats:
Bloecher, T.; Garralda Iriarte, L.; Schneider, J.; Zimmermann, C. and Stork, W. (2017). A Low-cost Life Sign Detection Method based on Time Series Analysis of Facial Feature Points.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 147-154. DOI: 10.5220/0006141601470154

@conference{biosignals17,
author={Timon Bloecher. and Leyre Garralda Iriarte. and Johannes Schneider. and Christoph Zimmermann. and Wilhelm Stork.},
title={A Low-cost Life Sign Detection Method based on Time Series Analysis of Facial Feature Points},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={147-154},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006141601470154},
isbn={978-989-758-212-7},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - A Low-cost Life Sign Detection Method based on Time Series Analysis of Facial Feature Points
SN - 978-989-758-212-7
AU - Bloecher, T.
AU - Garralda Iriarte, L.
AU - Schneider, J.
AU - Zimmermann, C.
AU - Stork, W.
PY - 2017
SP - 147
EP - 154
DO - 10.5220/0006141601470154

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