Evaluation of sensor calibration in a biometric person recognition framework based on sensor fusion

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 sensor normalization based on statistical sensor properties. We call this procedure sensor calibration. The final decision fusion simplifies to a multiplication or addition of the normalized outputs of each sensor. This approach is evaluated on a large database of 170 people with a total of 6315 recordings.