Extract DCT coefficients
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.
Endpoint Name | Data Format | Nature |
---|---|---|
image | system/array_2d_floats/1 | Input |
features | 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 |
---|---|---|---|---|
block-size | uint32 | 12 | ||
block-overlap | uint32 | 11 | ||
number-of-components | uint32 | 45 | ||
norm-blocks | bool | True | ||
norm-dcts | bool | True |
The code for this algorithm in Python
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Extract DCT features for the parts-based Face Recognition System described in [McCool2009], [McCool2013].
This algorithm relies on the Bob library.
The input, image, is a two-dimensional array of floats (64 bits) corresponding to one image. The outputs, features, is a two-dimensional array of floats (64 bits) corresponding to the DCT coeficients of each block.
[McCool2009] | McCool, Christopher, and Sébastien Marcel: Parts-based face verification using local frequency bands. Advances in Biometrics. Springer Berlin Heidelberg, 2009. 259-268. |
[McCool2013] | McCool, Christopher, et al. "Session variability modelling for face authentication." IET biometrics 2.3 (2013): 117-129. |
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.