Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework

Authors:
Bernhard Fröba;Constanze Rothe;Christian Küblbeck
Affiliations:
-;-;-
Venue:
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Year:
2000

Citing 5
Cited 0

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On Combining Classifiers

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Combining Evidence in Multimodal Personal Identity Recognition Systems

AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
SESAM: A Biometric Person Identification System Using Sensor Fusion

AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication

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Abstract

Biometric person authentication is a secure and user-friendly way of identifying persons in a variety of everyday applications. In order to achieve high recognition rates, we propose an audio-visual person recognition system based on voice, lip motion and still image. The combination of these three data sources (called sensor fusion) may be performed in several ways. We present a method for a sensor normalization based on statistical properties which we call sensor calibration. The final fusion simplifies to a multiplication or addition of the outputs of each sensor. This approach is evaluated on a large database of 170 people with a total of 6315 recordings which were recorded in at least two sessions per person.