Computes image-quality features for every frame of input video.

Forked from ivana7c/crop_face/1

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
video system/array_4d_uint8/1 Input
iqm_feature_set system/array_2d_floats/1 Output

The code for this algorithm in Python
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Given an input video consisting of N frames, this algorithm returns a Nxd floating-point array containing one d-dimensional feature-vector for each frame. The feature vector consists of various image-quality measures computed for the frame.

This algorithm relies on the Bob library.

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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