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

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... ognize, 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.