This algorithm is a sequential one. The platform will call its process() method once per data incoming on its inputs.

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
probe_image system/array_2d_uint8/1 Input
template_images system/array_3d_uint8/1 Input
score system/float/1 Output

Group: model

Endpoint Name Data Format Nature
linear_model tutorial/linear_machine/1 Input

The code for this algorithm in Python
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A biometrics algorithm that compares a probe image to a set of template images and outputs a comparison score. This algorithm was trained on the ATNT database and reproduces the EigenFaces face recognition baseline. The input images must be gray-scale and of the size of 92x92.


Updated Name Databases/Protocols Analyzers
amohammadi/amohammadi/atnt_eigenfaces/1/atnt1 atnt/6@idiap amohammadi/eer_analyzer/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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