Departamento de Informática da Universidade da Beira Interior








 Important Dates



 Registered Participants








SOCIA Lab. – Soft Computing and Image Analysis Group 

Department of Computer Science, University of Beira Interior, 6201-001 Covilhã, Portugal



NICE:II Evaluation



Let P denote the submitted application, which gives the dissimilarity between segmented iris images.

Let I={I1,…,In} be the data set containing the input iris images an let M={M1,…,Mn}be the corresponding binary maps that give the segmentation of the noise-free iris region.

P receives two iris images (and the corresponding binary maps) and outputs the dissimilarity value between the corresponding irises: P(Ii, Mi Ij, Mj) - - > D. D should be a real positive value.

Performing a “one-against-all” comparison scheme for each image of I gives a set of intra-class dissimilarity values DI={DI1,…,DIk} and a set of inter-class dissimilarity values DE={DE1,…,DEm}, whether the captured images are from the same or from different irises.

The decidability value  d’(DI1,…,DIk,DE1,…,DEm )- - > [0, ∞[ will be used as evaluation measure:


d’=| avg(DI) - avg(DE)| / sqrt ( 0.5* ( std(DI)2 + std(DE)2) )


where avg(DI) and avg(DE) denote the average values of the intra-class and inter-class comparisons and std(DI) and std(DE) the corresponding standard deviation values.

Participants of the NICE:II contest will be ranked from the highest (best) to the lowest (worst) decidability values.









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