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Image specific error rate: A biometric performance metric



Elham Tabassi


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
Conference Title
20th International Conference on Pattern Recognition ICPR)


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


Tabassi, E. (2010), Image specific error rate: A biometric performance metric, 20th International Conference on Pattern Recognition ICPR), Istanbul, -1, [online], (Accessed June 17, 2024)


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Created August 20, 2010, Updated February 19, 2017