BEAT - ivana7c/calclbp/1

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

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.

Unnamed group

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		uint32	1
num-neighbors		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) 
        
        lbphistlength = lbp.max_label

feat_vector = numpy.array(lbphistlength * [0.])

for i in range(frames.shape[0]):
          frame = frames[0]

lbpimage = numpy.ndarray(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
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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]

Parameters:

radius: Radius of the pixel neighborhood
num-neighbors: Number of neighbors to be considered

This algorithm relies on the Bob library.

[Ojala02]

Ojala, M. Pietikainen, T. 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:

Name	Databases/Protocols	Analyzers
smarcel/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms-fix	replay/3@grandtest	Kanma/iqm_spoof_eer_analyzer/1
sbhatta/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms	replay/2@grandtest	sbhatta/iqm_spoof_eer_analyzer/9
smarcel/ivana7c/simple-antispoofing-updated/1/antispoof-chi2-expA-rr22	replay/1@countermeasure	ivana7c/spoofing_eer/1
ivana7c/ivana7c/simple-antispoofing-updated/1/antispoof-chi2-expA	replay/1@countermeasure	ivana7c/spoofing_eer/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.

algorithms ivana7c calclbp 1

Endpoint Groups 1

Unnamed group

Parameters 2

Experiments

algorithms

ivana7c

calclbp

1