Perform a photometric normalization on the given (aligned) image

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: main

Endpoint Name Data Format Nature
image system/array_2d_uint8/1 Input
preprocessed system/array_2d_floats/1 Output

The code for this algorithm in Python
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The photometric normalization is accoring to [Tan07], which is implemented in bob.ip.base.TanTriggs. Although the original algorithm has some parameters, we here stick to the ones from [Tan07].

[Tan07](1, 2) Xiaoyang Tan and Bill Triggs. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. International Conference on Analysis and Modeling of Faces and Gestures, 2007


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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