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

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
featureSet system/array_2d_floats/1 Input
class system/text/1 Input
subspace tutorial/linear_machine/1 Output

The code for this algorithm in Python
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This algorithm will run Linear Discriminant Analysis [LDA] for a binary classification problem using images as inputs.

Inputs:
featureSet: a 2d float array of size Nxd, where N is the number of patterns and d is the length of each pattern. class: a text label. The label can take one of two values: 'real' or 'attack'
[LDA]http://en.wikipedia.org/wiki/Linear_Discriminant_analysis

Experiments

Updated Name Databases/Protocols Analyzers
sbhatta/sbhatta/iqm-face-antispoofing-test/2/replay2-antispoofing-iqm-lda replay/2@grandtest sbhatta/iqm_spoof_eer_analyzer/9

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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