Extracts local binary patterns in local histograms and concatenate these histograms
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Parameters allow users to change the configuration of an algorithm when scheduling an experiment
|block-overlap||The overlap of those block, must be smaller than the block-size||uint32||11|
|block-size||The size of the image blocks, from which local histograms should be extracted||uint32||12|
|left-eye-x||The horizontal position of the left eye (subject perspective) in the cropped image||uint32||48|
|left-eye-y||The vertical position of the left eye (subject perspective) in the cropped image||uint32||16|
|crop-height||The height of the cropped image||uint32||80|
|crop-width||The width of the cropped image||uint32||64|
|right-eye-x||The horizontal position of the right eye (subject perspective) in the cropped image||uint32||15|
|right-eye-y||The vertical position of the right eye (subject perspective) in the cropped image||uint32||16|
The code for this algorithm in Python
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This algorithms extracts Local Binary Pattern Histogram Sequence (LBPHS) features as introduced by [Ahonen04]. First, the image is aligned accorsing to the hand-labeled eye locations. Then, uniform circular Local Binary Patterns (LBPs) [Ojala96] are extracted from the aligned image. Afterwards, the image is split into (possibly overlapping) blocks, and a local histogram of LBP features is extracted for each block. Finally, all histograms are concatenated to form the full LBPHS feature vector.