Computes the similarity between a grid graph template and a grid graph probe

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.
This algorithm is splittable

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: probes

Endpoint Name Data Format Nature
comparison_ids system/array_1d_uint64/1 Input
probe siebenkopf/graph/1 Input
scores system/array_1d_floats/1 Output

Group: models

Endpoint Name Data Format Nature
model siebenkopf/graph_model/3 Input

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
gabor_jet_similarity The Gabor jet similarity function to be used string Canberra ScalarProduct, Canberra, Disparity, PhaseDiff, PhaseDiffPlusCanberra

The code for this algorithm in Python
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In this algorithm, the similarity between a template graph T (which is a concatenation of several enrollment graphs) and a probe sample P is computed. The similarities of all node positions n is simply averaged:

simtotal(T, P) = (1)/(N)nsimnode(Tn, Pn)

In each node, the similarity of all enrollment jets tm with the probe jet p is computed, and the maximum value is taken:

simnode(t, p) = maxmS(tm, p)

Where S is a Gabor jet similarity function, which can be chosen accordingg to [Guenther12].

[Guenther12]Manuel Günther, Denis Haufe, Rolf P. Würtz. Face recognition with disparity corrected Gabor phase differences. Artificial Neural Networks and Machine Learning, pp. 411-418, 2012.

Experiments

Updated Name Databases/Protocols Analyzers
siebenkopf/siebenkopf/FaceRec-WithOut-Training/2/XM2VTS-PhaseDiff xm2vts/1@darkened-lp1 siebenkopf/EER_HTER/8,siebenkopf/ROC/15
siebenkopf/siebenkopf/FaceRec-WithOut-Training/2/XM2VTS-ScalarProduct xm2vts/1@darkened-lp1 siebenkopf/EER_HTER/8,siebenkopf/ROC/15
siebenkopf/siebenkopf/FaceRec-WithOut-Training/2/XM2VTS-Canberra xm2vts/1@darkened-lp1 siebenkopf/EER_HTER/8,siebenkopf/ROC/15
siebenkopf/siebenkopf/FaceRec-WithOut-Training/2/Banca_P-ScalarProduct banca/1@P siebenkopf/EER_HTER/8,siebenkopf/ROC/15
siebenkopf/siebenkopf/FaceRec-WithOut-Training/2/Banca_P-Canberra banca/1@P siebenkopf/EER_HTER/8,siebenkopf/ROC/14
siebenkopf/siebenkopf/FaceRec-WithOut-Training/2/Banca_P-PhaseDiff banca/1@P siebenkopf/EER_HTER/8,siebenkopf/ROC/14

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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