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The proposed method reduces the false acceptance rate and false rejection rate in comparison to other standard correlation filtering techniques.
This causes the increase of the False Rejection Rate (genuine users are no more recognized) or the False Acceptance Rate (impostors are misclassified as genuine users), or both.
On the other hand, the pre-processing step, which inevitably destroys the existing feature distribution by denoising and contrast enhancement, is necessary in the traditional recognition framework and often brings in high false rejection rate.
The dissimilarity measure of each region are fused at the decision level by exploring 1-out-of-n: Accept (A) post-classifier in order to reduce the false rejection rate.
An optimized preferential digital optical correlator is proposed for multi-class face recognition, where false rejection rate and false acceptance rate are improved by incorporating the information preferentially of both intra-class and other class face images for a given database during the synthesis of the correlator filter.
False rejection rate.
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Created by Professor John Daugman – the original inventor of iris recognition and now L-1's chief scientist for Iris Recognition – the algorithm is claimed to reduce false rejection rates by as much as a factor of 10 and is designed to overcome image quality issues encountered in more challenging real-world scenarios.
For the verification task, we calculated false acceptance rates (FARs) and false rejection rates (FRRs).
The simplest upper bound for the false acceptance and the false rejection rates can be expressed using the Bhattacharyya distance [13] between the probability density functions and.
The study was conducted in terms of false acceptance and false rejection rates, regardless of the proper functioning of the certain stages of the method.
However, with few notable exceptions, for example, [15, 19], with small false rejection rates, the average FAR of 15% has been cited.
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