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Updated paper name in files
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birfan committed Jun 9, 2021
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5 changes: 2 additions & 3 deletions scripts/dataset/artificialDataset.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# artificialDataset, RecognitionMemory and each script in this project is under the GNU General Public #
# License. #
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5 changes: 2 additions & 3 deletions scripts/dataset/imdb_face_crossval_extraction.m
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% images in the dataset. %
% %
% Please cite the following work if using this code: %
% B. Irfan, N. Lyubova, M. Garcia Ortiz, and T. Belpaeme (2018), 'Multi-modal Open-Set Person %
% Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social %
% Robots in the Wild workshop. %
% B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User %
% Identification', ACM Transactions on Human-Robot Interaction (THRI). %
% %
% * Face cropped images of IMDB dataset in IMDB-Wiki dataset are used for this purpose: %
% R. Rothe and R. Timofte and L. Van Gool (2016), 'Deep expectation of real and apparent age from a %
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5 changes: 2 additions & 3 deletions scripts/dataset/imdb_prepareImages.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# * Face cropped images of IMDB dataset in IMDB-Wiki dataset are used for this purpose: #
# R. Rothe and R. Timofte and L. Van Gool (2016), 'Deep expectation of real and apparent age from a #
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17 changes: 17 additions & 0 deletions scripts/dataset/mmltur.py
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# coding: utf-8

#! /usr/bin/env python

#========================================================================================================#
# Copyright (c) 2017-present, Bahar Irfan #
# #
# mmltur script creates a smaller version of the dataset, using the 6th repeat second fold (used for the#
# evaluations). #
# #
# Please cite the following work if using this code: #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# Each script in this project is under the GNU General Public License. #
#========================================================================================================#

import pandas
import csv
import os
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5 changes: 2 additions & 3 deletions scripts/evm/evm_IMDB.py
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# Recognition Dataset. #
# #
# Please cite the following work if using this code: #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer and Terrance E. Boult (2018), "The Extreme Value #
# Machine" in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 3. #
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5 changes: 2 additions & 3 deletions scripts/mmibn/RecognitionMemory.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# The pyAgrum library is used for implementing the Bayesian network structure: #
# Gonzales, Christophe and Torti, Lionel and Wuillemin, Pierre-Henri (2017), 'aGrUM: a Graphical #
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5 changes: 2 additions & 3 deletions scripts/mmibn/WeightOptimisation.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# * Bayesian optimisation is a variant of the code by Thomas Huijskens: #
# https://github.com/thuijskens/bayesian-optimization #
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5 changes: 2 additions & 3 deletions scripts/mmibn/crossValidation.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# crossValidation, RecognitionMemory and each script in this project is under the GNU General Public #
# License. #
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5 changes: 2 additions & 3 deletions scripts/mmibn/run_CrossValidationOnRobot.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# This script, RecognitionMemory and each script in this project is under the GNU General Public #
# License. #
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5 changes: 2 additions & 3 deletions scripts/mmibn/run_KFoldCrossValidation.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# This script, RecognitionMemory and each script in this project is under the GNU General Public #
# License. #
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5 changes: 2 additions & 3 deletions scripts/mmibn/run_TestOnRobotValidation.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# This script, RecognitionMemory and each script in this project is under the GNU General Public #
# License. #
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16 changes: 16 additions & 0 deletions scripts/other/pyagrum_structure_learning.py
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# coding: utf-8

#! /usr/bin/env python

#========================================================================================================#
# Copyright (c) 2017-present, Bahar Irfan #
# #
# pyagrum_structure_learning applies structure learning on the Multi-modal Long-Term Recognition Dataset#
# #
# Please cite the following work if using this code: #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# Each script in this project is under the GNU General Public License. #
#========================================================================================================#

import pyAgrum as gum
import os
import pandas as pd
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18 changes: 10 additions & 8 deletions scripts/other/run_EvaluateSystem.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# This script, RecognitionMemory and each script in this project is under the GNU General Public #
# License. #
Expand Down Expand Up @@ -55,7 +54,7 @@ def plotConfusionMatrix(results_folder, conf_matrix_file_set):
num_people = 100

update_methods = ["none", "evidence"]
dest_main = "ColombiaExperiment/"
dest_main = "HRIExperiment/"
normMethod = "hybrid"
db_file = "db_data.csv"
init_recog_file = "InitialRecognition_data.csv"
Expand All @@ -71,6 +70,10 @@ def plotConfusionMatrix(results_folder, conf_matrix_file_set):

optim_values = pandas.read_csv(dest_main + optim_file, dtype={"Evidence_method": object, "Norm_method": object, "Optim_params":object}, usecols = {"Evidence_method", "Norm_method", "Optim_params"})

with open(dest_main + stats_file, 'w') as outcsv:
writer = csv.writer(outcsv)
writer.writerow(["Model","FAR","FR_FAR","DIR","FR_DIR","Loss","FR_Loss","Num_recog","Num_enrolled","Total_Time"])

for updateMethod in update_methods:
start_time = time.time()
if updateMethod == "evidence":
Expand All @@ -89,22 +92,21 @@ def plotConfusionMatrix(results_folder, conf_matrix_file_set):
weights.insert(0, faceWeight)

RB = RecognitionMemory.RecogniserBN()
start_time_run = time.time()
num_recog, FER, stats_openSet, stats_FR, num_unknown = RB.runCrossValidation(num_people, results_folder, None,
None, None, None, None, isTestData = False, isOpenSet = False,
weights = weights, faceRecogThreshold = None, qualityThreshold = qualityThreshold, normMethod = normMethod, updateMethod = updateMethod, probThreshold = None,
isMultRecognitions = False, num_mult_recognitions = 3, qualityCoefficient = None,
db_file = db_file_model, init_recog_file = init_recog_file_model, final_recog_file = final_recog_file_model, valid_info_file = None,
isSaveRecogFiles = isSaveRecogFiles, isSaveImageAn = isSaveImageAn)

time_run = time.time()-start_time_run
loss = cost_function_alpha*(1.0 - stats_openSet[0]) + (1.0-cost_function_alpha)*(stats_openSet[1])
F_Loss = cost_function_alpha*(1.0 - stats_FR[0]) + (1.0-cost_function_alpha)*(stats_FR[1])

with open(dest_main + stats_file, 'a') as outcsv:
writer = csv.writer(outcsv)
writer.writerow([model_name, stats_openSet[1], stats_FR[1], stats_openSet[0], stats_FR[0], loss, F_Loss, num_recog, num_unknown])
writer.writerow([model_name, stats_openSet[1], stats_FR[1], stats_openSet[0], stats_FR[0], loss, F_Loss, num_recog, num_unknown, time_run])

for matrix_f in ["Network", "FaceRecognition"]:
conf_file = conf_matrix_file.replace(".csv", matrix_f + ".csv")
plotConfusionMatrix(results_folder, conf_file)

print "time for " + model_name + ":" + str(time.time() - start_time)
5 changes: 2 additions & 3 deletions scripts/other/run_KFoldCrossValidation.py
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# Identification in HRI', 2018 ACM/IEEE International Conference on Human-Robot Interaction Social #
# Robots in the Wild workshop. #
# #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Incremental #
# Bayesian Network with Online Learning for Open World User Identification', ACM Transactions on #
# Human-Robot Interaction (THRI). #
# B. Irfan, M. Garcia Ortiz, N. Lyubova, and T. Belpaeme (under review), 'Multi-modal Open World User #
# Identification', ACM Transactions on Human-Robot Interaction (THRI). #
# #
# This script, RecognitionMemory and each script in this project is under the GNU General Public #
# License. #
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