BEAT - anjos/calclbp/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

Endpoint Groups 1

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
input_frames	system/array_3d_uint8/1	Input
lbp_feature_vector	system/array_1d_floats/1	Output

Parameters 2

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

Name	Description	Type	Default	Range/Choices
radius	LBP radius in pixels	uint32	1
num-neighbors	Number of points to be used for the LBP operator	uint32	8

import numpy
import bob

class Algorithm:

def __init__(self):
        self.radius = 1
        self.num_neighbors = 8

def setup(self, parameters):
        self.radius = int(parameters.get('radius', self.radius))
        self.num_neighbors  = int(parameters.get('num-neighbors',  self.num_neighbors))

return True

def process(self, inputs, outputs):

frames = inputs["input_frames"].data.value

lbp = bob.ip.LBP(neighbors=self.num_neighbors, uniform=True, circular=True, radius=self.radius)
        feat_vector = numpy.zeros(lbp.max_label, dtype='float64')

for i in range(frames.shape[0]):
          frame = frames[0]
          lbpimage = numpy.zeros(lbp.get_lbp_shape(frame), 'uint16') # allocating the image with lbp codes
          lbp(frame, lbpimage) # calculating the lbp image
          hist = bob.ip.histogram(lbpimage, 0, lbp.max_label-1, lbp.max_label)
          hist = hist / sum(hist)
          feat_vector  = feat_vector + hist

feat_vector = feat_vector / frames.shape[0]
        
        outputs["lbp_feature_vector"].write({
            'value': feat_vector,
        })

return True

The code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

This algorithm computes LBP-based feature vector. For a given input set of frames in a video, it computes the normalized LBP histogram. Then, it computes the final feature vector on video level, by averaging the normalized LBP histograms.

The used LBP operator is computed over a circular neighborhood and is uniform. Details about LBP operator can be found in [Ojala02]

This algorithm relies on the Bob library.

[Ojala02]

Ojala, Pietikainen and Maenpaa: Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine inteligence 2002

Experiments

Attestation:

Privacy:

Status:

			Updated	Name	Databases/Protocols	Analyzers
				anjos/ivana7c/simple-antispoofing-updated/1/face-antipoofing-lbp-histogram-comparison	replay/1@countermeasure	anjos/antispoofing_analyzer/1

Scientific Python 2.7 (0.0.4) 15

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.

algorithms anjos calclbp 1

Endpoint Groups 1

Group: main

Parameters 2

Experiments

algorithms

anjos

calclbp

1