Evaluation of Sensor Calibration in a Biometric
Person Recognition Framework based on Sensor Fusion
Bernhard Fröba, Constanze Rothe, Christian Küblbeck
Abstract
Biometric person authentication is a secure and user-friendly way
to identify 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. In this paper we compare
different approaches to this problem. 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 then compared
to a traditional fusion method which handles fusion as a
classification problem. These two methods are compared with the voting
based fusion system presented in an earlier version of our system.