Tan and Triggs preprocessing

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.

Unnamed group

Endpoint Name Data Format Nature
image_gray system/array_2d_uint8/1 Input
tantriggs_image system/array_2d_floats/1 Output

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

Name Description Type Default Range/Choices
sigma0 float64 1.0
sigma1 float64 2.0
gamma float64 0.2
kernel_size int64 5
threshold float64 10.0
alpha float64 0.1

The code for this algorithm in Python
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This algorithm is a multi-stage image preprocessor relying on the Bob library. It implements the multi-stage preprocessing method described in [Tan07], the steps being a gamma correction, a difference of Gaussian filtering and a contrast equalization

Both inputs and outputs are expected to be grayscale images as two-dimensional arrays of floats (64 bits).

  1. Tan, B. Triggs: Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. Analysis and Modeling of Faces and Gestures (2007) 168-182


Updated Name Databases/Protocols Analyzers
martabarrero/smarcel/full_isv/1/Prueba_ISV_2 banca/1@Mc tutorial/eerhter_postperf/1

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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