BEAT - jmcgrat3/Open-Source-Iris-PAD/1

This algorithm is a sequential one. The platform will call its process() method once per data incoming on its inputs.

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
bsifs11b12	system/array_1d_uint32/1	Input
bsifs15b12	system/array_1d_uint32/1	Input
bsifs17b10	system/array_1d_uint32/1	Input
bsifs17b12	system/array_1d_uint32/1	Input
bsifs22b12	system/array_1d_uint32/1	Input
bsifs5b9	system/array_1d_uint32/1	Input
bsifs7b12	system/array_1d_uint32/1	Input
score	system/integer/1	Output

"""
Author: Joe McGrath
Date: 5/27/2020
Open Source Iris PAD implementation for the BEAT platform
"""
import numpy as np
import cv2 as cv

class Algorithm:
    def setup(self,parameters):
        """Called once"""
        #  Load the models from .xml strings
        self.svm_11_12 = cv.ml.SVM_create()
        svm_11_12_file = cv.FileStorage(get_svm_11_12(), cv.FileStorage_READ | cv.FileStorage_MEMORY)
        self.svm_11_12.read(svm_11_12_file.getFirstTopLevelNode())

self.svm_7_12 = cv.ml.SVM_create()
        svm_7_12_file = cv.FileStorage(get_svm_7_12(), cv.FileStorage_READ | cv.FileStorage_MEMORY)
        self.svm_7_12.read(svm_7_12_file.getFirstTopLevelNode())

self.svm_22_12 = cv.ml.SVM_create()
        svm_22_12_file = cv.FileStorage(get_svm_22_12(), cv.FileStorage_READ | cv.FileStorage_MEMORY)
        self.svm_22_12.read(svm_22_12_file.getFirstTopLevelNode())

self.svm_17_10 = cv.ml.SVM_create()
        svm_17_10_file = cv.FileStorage(get_svm_17_10(), cv.FileStorage_READ | cv.FileStorage_MEMORY)
        self.svm_17_10.read(svm_17_10_file.getFirstTopLevelNode())

self.svm_5_9 = cv.ml.SVM_create()
        svm_5_9_file = cv.FileStorage(get_svm_5_9(), cv.FileStorage_READ | cv.FileStorage_MEMORY)
        self.svm_5_9.read(svm_5_9_file.getFirstTopLevelNode())

self.svm_15_12 = cv.ml.SVM_create()
        svm_15_12_file = cv.FileStorage(get_svm_15_12(), cv.FileStorage_READ | cv.FileStorage_MEMORY)
        self.svm_15_12.read(svm_15_12_file.getFirstTopLevelNode())

self.svm_17_12 = cv.ml.SVM_create()
        svm_17_12_file = cv.FileStorage(get_svm_17_12(), cv.FileStorage_READ | cv.FileStorage_MEMORY)
        self.svm_17_12.read(svm_17_12_file.getFirstTopLevelNode())

return True

def process(self, inputs, data_loaders, outputs):
        """Called for every image"""
        #  Load the histograms and normalize them (z-score) (hist_scale_depth)
        #   hist_scale_bits
        hist_11_12 = normalize_hist(inputs['bsifs11b12'].data.value)[None,:]
        hist_7_12 = normalize_hist(inputs['bsifs7b12'].data.value)[None,:]
        hist_22_12 = normalize_hist(inputs['bsifs22b12'].data.value)[None,:]
        hist_17_10 = normalize_hist(inputs['bsifs17b10'].data.value)[None,:]
        hist_5_9 = normalize_hist(inputs['bsifs5b9'].data.value)[None,:]
        hist_15_12 = normalize_hist(inputs['bsifs15b12'].data.value)[None,:]
        hist_17_12 = normalize_hist(inputs['bsifs17b12'].data.value)[None,:]
        # Make predictions
        spoof_votes = 0
        prediction = self.svm_11_12.predict(hist_11_12)[1] # returns retval,results
        if prediction == 1:
            spoof_votes += 1
        prediction = self.svm_7_12.predict(hist_7_12)[1]
        if prediction == 1:
            spoof_votes += 1
        prediction = self.svm_22_12.predict(hist_22_12)[1]
        if prediction == 1:
            spoof_votes += 1
        prediction = self.svm_17_10.predict(hist_17_10)[1]
        if prediction == 1:
            spoof_votes += 1
        prediction = self.svm_5_9.predict(hist_5_9)[1]
        if prediction == 1:
            spoof_votes += 1
        prediction = self.svm_15_12.predict(hist_15_12)[1]
        if prediction == 1:
            spoof_votes += 1
        prediction = self.svm_17_12.predict(hist_17_12)[1]
        if prediction == 1:
            spoof_votes += 1

# calculate score (100 = greatest degree of liveness)
        score = (1 - spoof_votes/7) * 100

# write outputs
        outputs['score'].write({'value': np.cast["int32"](score)})
        print("finished writing outputs")
        return True

def normalize_hist(histogram):
    """Computes the z-score for a histogram"""
    mean = np.mean(histogram)
    stddev = np.std(histogram)
    histogram = (histogram - mean)/stddev
    return np.float32(histogram)

def get_svm_11_12():
    return """<?xml version="1.0"?>
<opencv_storage>
<opencv_ml_svm>
  <format>3</format>
  <svmType>C_SVC</svmType>
  <kernel>
    <type>RBF</type>
    <gamma>1.5000000000000001e-04</gamma></kernel>
  <C>6.2500000000000000e+01</C>
  <term_criteria><epsilon>1.1920928955078125e-07</epsilon>
    <iterations>1000</iterations></term_criteria>
  <var_count>4096</var_count>
  <class_count>2</class_count>
  <class_labels type_id="opencv-matrix">
    <rows>2</rows>
    <cols>1</cols>
    <dt>i</dt>
    <data>
      0 1</data></class_labels>
  <sv_total>752</sv_total>
  <support_vectors>
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algorithms jmcgrat3 Open-Source-Iris-PAD 1

Endpoint Groups 1

Group: main

algorithms

jmcgrat3

Open-Source-Iris-PAD

1