Image specific error rate: A biometric performance metric

Published

August 20, 2010

Author(s)

Elham Tabassi

Abstract

Image-specific false match and false non-match error rates are defined by inheriting concepts from the biometric zoo. These metrics support failure mode analyses by allowing association of a covariate (e.g., dilation for iris recognition) with a matching error rate without having to consider the covariate of a comparison image. Image-specific error rates are also useful in detection of ground truth errors in test datasets. Images with higher image-specific error rates are more difficult to recognize, so these metircs can be used to assess the level of difficulty of test corpora or partition a corpus into sets with varying level of difficulty. Results on use of image-specific error rates for ground-truth error detection, covariate analysis and corpus partitioning is presented.sion on a per-user basis.

Conference Dates

August 22-26, 2010

Conference Location

Istanbul

Conference Title

20th International Conference on Pattern Recognition ICPR)

Pub Type

Conferences

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Keywords

biometric, performance, evaluation, security, error rate, quality

Citation

Tabassi, E. (2010), Image specific error rate: A biometric performance metric, 20th International Conference on Pattern Recognition ICPR), Istanbul, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=904876 (Accessed October 9, 2025)