False Negative Rate means that how many percentages of the authentic test samples are incorrectly classified as the imposter class. Take the example of the computer account login system, False Negative Rate means how many percentages of legal users are recognized as illegal users. As one can see immediately, False Positive Rate and False Negative Rate are two metrics that counter each other. For any given biometrics modality with given matching algorithm, requirement of low False Positive Rate would unavoidably bring high False Negative Rate, and vice versa. Performance comparison between different algorithms is usually done by comparing False Negative Rate at a fixed False Positive Rate.
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(2009). False Negative Rate. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_1095
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DOI: https://doi.org/10.1007/978-0-387-73003-5_1095
Publisher Name: Springer, Boston, MA
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