Extracts local binary patterns in local histograms and concatenate these histograms

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
image system/array_3d_uint8/1 Input
eye_centers system/eye_positions/1 Input
features system/array_1d_floats/1 Output

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
lbp_neighbours Number of neighbours considered for the LBP bin computation uint32 8
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
lbp_radius Radius considered in the LBP bin computation uint32 1
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.

[Ojala96]
  1. Ojala, M. Pietikainen, D. Harwood: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29 (1996) 51-59
[Ahonen04]
  1. Ahonen, A. Hadid, M. Pietikainen: Face Recognition with Local Binary Patterns. ECCV (1) 2004: 469-481

Experiments

Updated Name Databases/Protocols Analyzers
tpereira/tutorial/full_lbphs/2/btas2015_LBP-baseline_periocular_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tutorial/full_lbphs/2/btas2015_LBP-baseline_periocular_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/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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