2011
DOI: 10.6028/nist.ir.7758
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An introduction to the good, the bad, & the ugly face recognition challenge problem

Abstract: The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, & the Ugly consists of three partitions. The Good partition contains pairs of images that are considered easy to recognize. On the Good partition, the base verification rate (VR) is 0.98 at a false accept rate (FAR) of 0.001. The Bad partition contains pairs of images of average difficulty to recognize. Fo… Show more

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Cited by 86 publications
(121 citation statements)
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“…The CSU baseline algorithm suite includes two recently published algorithms, local region PCA (LRPCA) [17] and LDA with color spaces and cohort normalization (CohortLDA) [16]. The framework is written in Python and R with installation instructions provided for Mac and Windows.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…The CSU baseline algorithm suite includes two recently published algorithms, local region PCA (LRPCA) [17] and LDA with color spaces and cohort normalization (CohortLDA) [16]. The framework is written in Python and R with installation instructions provided for Mac and Windows.…”
Section: Related Workmentioning
confidence: 99%
“…The framework is written in Python and R with installation instructions provided for Mac and Windows. Scripts are included to run the algorithms against the GBU [17] and LFW [8] datasets, though programming language requirements and the lack of a well defined API make it difficult to incorporate their work into new applications. Source code is released as a zipped archive and it is unclear how developers should contribute back to the project.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…This section evaluates the effects of targeted attacks on the CSU baseline algorithm developed by Bolme et al [15] for the good, the bad and the ugly face recognition challenge [16]. The system has been trained using images from the NIST multiple biometric grand challenge data set [17].…”
Section: Impact Evaluationmentioning
confidence: 99%
“…To show their advantage over other existing algorithms, face recognition experiments have typically been executed on one or more publicly available facial image databases [1,2,3,4,5,6,7,8]. Unfortunately, often these databases are not accompanied by strict experimental protocols or the protocols that are provided are biased.…”
Section: Introductionmentioning
confidence: 99%