BEAT - amohammadi/livdet

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

This algorithm is an analyzer. It can only be used on analysis blocks.

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
scores	system/integer/1	Input
category	system/integer/1	Input

Results 25

Analyzers may produce any number of results. Once experiments using this analyzer are done, you may display the results or filter experiments using criteria based on them.

Name	Type
rocAll	plot/isoroc/1
numberCorrectT5Samples	int32
totalNumberT1Samples	int32
totalNumberT5Samples	int32
totalNumberT3Samples	int32
numberNotProcessedT4Samples	int32
totalNumberT6Samples	int32
totalNumberT2Samples	int32
numberCorrectT3Samples	int32
rocT1T3	plot/isoroc/1
ACER	float32
numberNotProcessedT5Samples	int32
rocT1T2	plot/isoroc/1
numberNotProcessedT6Samples	int32
numberNotProcessedT3Samples	int32
numberCorrectT6Samples	int32
numberCorrectT2Samples	int32
rocT1T6	plot/isoroc/1
numberCorrectT4Samples	int32
numberNotProcessedT2Samples	int32
totalNumberT4Samples	int32
numberCorrectT1Samples	int32
rocT1T4	plot/isoroc/1
numberNotProcessedT1Samples	int32
rocT1T5	plot/isoroc/1

import numpy as np
import bob.measure

class Algorithm:
    def __init__(self):
        self.T1 = None
        self.T2 = None
        self.T3 = None
        self.T4 = None
        self.T5 = None
        self.T6 = None
        self.allspoofs = None
        self.correct_T1 = []
        self.correct_T2 = []
        self.correct_T3 = []
        self.correct_T4 = []
        self.correct_T5 = []
        self.correct_T6 = []
        self.incorrect_T1 = []
        self.incorrect_T2 = []
        self.incorrect_T3 = []
        self.incorrect_T4 = []
        self.incorrect_T5 = []
        self.incorrect_T6 = []
        self.notprocessed_T1 = []
        self.notprocessed_T2 = []
        self.notprocessed_T3 = []
        self.notprocessed_T4 = []
        self.notprocessed_T5 = []
        self.notprocessed_T6 = []
        self.acer = None
        self.n_bins = 50
        self.threshold = 50

def process(self, inputs, data_loaders, outputs):
        # accumulate the test scores
        data_test = inputs["scores"].data.value
        label_test = inputs["category"].data.value

if label_test == 1:
            if data_test < 0 or data_test > 100:
                self.notprocessed_T1.append(data_test)
            elif data_test > self.threshold:
                self.correct_T1.append(data_test)
            else:
                self.incorrect_T1.append(data_test)
        elif label_test == 2:
            if data_test < 0 or data_test > 100:
                self.notprocessed_T2.append(data_test)
            elif data_test <= self.threshold:
                self.correct_T2.append(data_test)
            else:
                self.incorrect_T2.append(data_test)
        elif label_test == 3:
            if data_test < 0 or data_test > 100:
                self.notprocessed_T3.append(data_test)
            elif data_test <= self.threshold:
                self.correct_T3.append(data_test)
            else:
                self.incorrect_T3.append(data_test)
        elif label_test == 4:
            if data_test < 0 or data_test > 100:
                self.notprocessed_T4.append(data_test)
            elif data_test <= self.threshold:
                self.correct_T4.append(data_test)
            else:
                self.incorrect_T4.append(data_test)
        elif label_test == 5:
            if data_test < 0 or data_test > 100:
                self.notprocessed_T5.append(data_test)
            elif data_test <= self.threshold:
                self.correct_T5.append(data_test)
            else:
                self.incorrect_T5.append(data_test)
        elif label_test == 6:
            if data_test < 0 or data_test > 100:
                self.notprocessed_T6.append(data_test)
            elif data_test <= self.threshold:
                self.correct_T6.append(data_test)
            else:
                self.incorrect_T6.append(data_test)

# once all values are received, evaluate the scores
        if not (inputs.hasMoreData()):
            self.T1 = np.array(self.correct_T1 + self.incorrect_T1)
            self.T2 = np.array(self.correct_T2 + self.incorrect_T2)
            self.T3 = np.array(self.correct_T3 + self.incorrect_T3)
            self.T4 = np.array(self.correct_T4 + self.incorrect_T4)
            self.T5 = np.array(self.correct_T5 + self.incorrect_T5)
            self.T6 = np.array(self.correct_T6 + self.incorrect_T6)
            self.allspoofs = np.array(
                self.correct_T2
                + self.incorrect_T2
                + self.correct_T3
                + self.incorrect_T3
                + self.correct_T4
                + self.incorrect_T4
                + self.correct_T5
                + self.incorrect_T5
                + self.correct_T6
                + self.incorrect_T6
            )

apcer = np.float32(
                len(
                    self.incorrect_T2
                    + self.incorrect_T3
                    + self.incorrect_T4
                    + self.incorrect_T5
                    + self.incorrect_T6
                )
                / len(self.allspoofs)
            )
            bpcer = np.float32(len(self.incorrect_T1) / len(self.T1))
            self.acer = np.float32((apcer + bpcer) / 2)

# test_hist_pos, test_bin_pos = np.histogram(self.lives, self.n_bins)
            # test_hist_neg, test_bin_neg = np.histogram(self.spoofs, self.n_bins)

# eer_threshold = bob.measure.eer_threshold(self.spoofs.astype(np.double), self.lives.astype(np.double))
            # far_test, frr_test = bob.measure.farfrr(self.negatives_test, self.lives, eer_threshold)

rocAll = bob.measure.roc(
                self.T1.astype(np.double), self.allspoofs.astype(np.double), 100
            )
            rocT1T2 = bob.measure.roc(
                self.T1.astype(np.double), self.T2.astype(np.double), 100
            )
            rocT1T3 = bob.measure.roc(
                self.T1.astype(np.double), self.T3.astype(np.double), 100
            )
            rocT1T4 = bob.measure.roc(
                self.T1.astype(np.double), self.T4.astype(np.double), 100
            )
            rocT1T5 = bob.measure.roc(
                self.T1.astype(np.double), self.T5.astype(np.double), 100
            )
            rocT1T6 = bob.measure.roc(
                self.T1.astype(np.double), self.T6.astype(np.double), 100
            )

# writes the output back to the platform
            outputs.write(
                {
                    "totalNumberT1Samples": np.int32(len(self.T1)),
                    "totalNumberT2Samples": np.int32(len(self.T2)),
                    "totalNumberT3Samples": np.int32(len(self.T3)),
                    "totalNumberT4Samples": np.int32(len(self.T4)),
                    "totalNumberT5Samples": np.int32(len(self.T5)),
                    "totalNumberT6Samples": np.int32(len(self.T6)),
                    "numberCorrectT1Samples": np.int32(len(self.correct_T1)),
                    "numberCorrectT2Samples": np.int32(len(self.correct_T2)),
                    "numberCorrectT3Samples": np.int32(len(self.correct_T3)),
                    "numberCorrectT4Samples": np.int32(len(self.correct_T4)),
                    "numberCorrectT5Samples": np.int32(len(self.correct_T5)),
                    "numberCorrectT6Samples": np.int32(len(self.correct_T6)),
                    "numberNotProcessedT1Samples": np.int32(len(self.notprocessed_T1)),
                    "numberNotProcessedT2Samples": np.int32(len(self.notprocessed_T2)),
                    "numberNotProcessedT3Samples": np.int32(len(self.notprocessed_T3)),
                    "numberNotProcessedT4Samples": np.int32(len(self.notprocessed_T4)),
                    "numberNotProcessedT5Samples": np.int32(len(self.notprocessed_T5)),
                    "numberNotProcessedT6Samples": np.int32(len(self.notprocessed_T6)),
                    "ACER": np.float32(self.acer),
                    "rocAll": {
                        "data": [
                            {
                                "label": "rocAll",
                                "false_positives": rocAll[1],
                                "false_negatives": rocAll[0],
                                "number_of_positives": np.uint64(len(self.T1)),
                                "number_of_negatives": np.uint64(len(self.allspoofs)),
                            }
                        ]
                    },
                    "rocT1T2": {
                        "data": [
                            {
                                "label": "rocT1T2",
                                "false_positives": rocT1T2[1],
                                "false_negatives": rocT1T2[0],
                                "number_of_positives": np.uint64(len(self.T1)),
                                "number_of_negatives": np.uint64(len(self.T2)),
                            }
                        ]
                    },
                    "rocT1T3": {
                        "data": [
                            {
                                "label": "rocT1T3",
                                "false_positives": rocT1T3[1],
                                "false_negatives": rocT1T3[0],
                                "number_of_positives": np.uint64(len(self.T1)),
                                "number_of_negatives": np.uint64(len(self.T3)),
                            }
                        ]
                    },
                    "rocT1T4": {
                        "data": [
                            {
                                "label": "rocT1T4",
                                "false_positives": rocT1T4[1],
                                "false_negatives": rocT1T4[0],
                                "number_of_positives": np.uint64(len(self.T1)),
                                "number_of_negatives": np.uint64(len(self.T4)),
                            }
                        ]
                    },
                    "rocT1T5": {
                        "data": [
                            {
                                "label": "rocT1T5",
                                "false_positives": rocT1T5[1],
                                "false_negatives": rocT1T5[0],
                                "number_of_positives": np.uint64(len(self.T1)),
                                "number_of_negatives": np.uint64(len(self.T5)),
                            }
                        ]
                    },
                    "rocT1T6": {
                        "data": [
                            {
                                "label": "rocT1T6",
                                "false_positives": rocT1T6[1],
                                "false_negatives": rocT1T6[0],
                                "number_of_positives": np.uint64(len(self.T1)),
                                "number_of_negatives": np.uint64(len(self.T6)),
                            }
                        ]
                    },
                }
            )

return True

The code for this algorithm in Python
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Experiments

Attestation:

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Status:

			Updated	Name	Databases/Protocols	Analyzers
				amohammadi/amohammadi/livdet/1/livdet	livdet-iris-2020/1@Main	amohammadi/livdet_analyzer/1

Python (2.1.0) 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 amohammadi livdet_analyzer 1

Endpoint Groups 1

Group: main

Results 25

Experiments

algorithms

amohammadi

livdet_analyzer

1