Bayesian fusion in multimodal biometrics computes the final decision by:
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A
Averaging the match scores from all enrolled templates
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B
Combining likelihood ratios using Bayes' theorem to estimate posterior probability of identity
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C
Selecting the matcher with the highest individual EER
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D
Normalizing all feature vectors to unit length before matching