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.
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Fröba, B., Rothe, C., Küblbeck, C. (2000). Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_35
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DOI: https://doi.org/10.1007/3-540-45014-9_35
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