Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Charbonneau, I.; Duncan, J.; Blais, C.; Guérette, J.; Plouffe-Demers, M. -P.; Smith, F.; Fiset, D.
Facial expression categorization predominantly relies on mid-spatial frequencies Journal Article
In: Vision Research, vol. 231, 2025, ISSN: 00426989 (ISSN), (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: adult, article, Bubbles, Classification, controlled study, emotion, Emotions, Facial Expression, facial expressions, Facial Recognition, female, human, Humans, male, physiology, Psychophysics, simulation, Spatial frequencies, Young Adult
@article{charbonneau_facial_2025,
title = {Facial expression categorization predominantly relies on mid-spatial frequencies},
author = {I. Charbonneau and J. Duncan and C. Blais and J. Guérette and M. -P. Plouffe-Demers and F. Smith and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003427898&doi=10.1016%2Fj.visres.2025.108611&partnerID=40&md5=508d315d8092a9142c2d82f1b774cfdb},
doi = {10.1016/j.visres.2025.108611},
issn = {00426989 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Vision Research},
volume = {231},
abstract = {Facial expressions are crucial in human communication. Recent decades have seen growing interest in understanding the role of spatial frequencies (SFs) in emotion perception in others. While some studies have suggested a preferential treatment of low versus high SFs, the optimal SFs for recognizing basic facial expressions remain elusive. This study, conducted on Western participants, addresses this gap using two complementary methods: a data-driven method (Exp. 1) without arbitrary SF cut-offs, and a more naturalistic method (Exp. 2) simulating variations in viewing distance. Results generally showed a preponderant role of low over high SFs, but particularly stress that facial expression categorization mostly relies on mid-range SF content (i.e. ∼6–13 cycles per face), often overlooked in previous studies. Optimal performance was observed at short to medium viewing distances (1.2–2.4 m), declining sharply with increased distance, precisely when mid-range SFs were no longer available. Additionally, our data suggest variations in SF tuning profiles across basic facial expressions and nuanced contributions from low and mid SFs in facial expression processing. Most importantly, it suggests that any method that removes mid-SF content has the downfall of offering an incomplete account of SFs diagnosticity for facial expression recognition. © 2025 The Authors},
note = {Publisher: Elsevier Ltd},
keywords = {adult, article, Bubbles, Classification, controlled study, emotion, Emotions, Facial Expression, facial expressions, Facial Recognition, female, human, Humans, male, physiology, Psychophysics, simulation, Spatial frequencies, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Facial expressions are crucial in human communication. Recent decades have seen growing interest in understanding the role of spatial frequencies (SFs) in emotion perception in others. While some studies have suggested a preferential treatment of low versus high SFs, the optimal SFs for recognizing basic facial expressions remain elusive. This study, conducted on Western participants, addresses this gap using two complementary methods: a data-driven method (Exp. 1) without arbitrary SF cut-offs, and a more naturalistic method (Exp. 2) simulating variations in viewing distance. Results generally showed a preponderant role of low over high SFs, but particularly stress that facial expression categorization mostly relies on mid-range SF content (i.e. ∼6–13 cycles per face), often overlooked in previous studies. Optimal performance was observed at short to medium viewing distances (1.2–2.4 m), declining sharply with increased distance, precisely when mid-range SFs were no longer available. Additionally, our data suggest variations in SF tuning profiles across basic facial expressions and nuanced contributions from low and mid SFs in facial expression processing. Most importantly, it suggests that any method that removes mid-SF content has the downfall of offering an incomplete account of SFs diagnosticity for facial expression recognition. © 2025 The Authors
2.
Charbonneau, I.; Duncan, J.; Blais, C.; Guérette, J.; Plouffe-Demers, M. -P.; Smith, F.; Fiset, D.
Facial expression categorization predominantly relies on mid-spatial frequencies Journal Article
In: Vision Research, vol. 231, 2025, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Bubbles, Classification, controlled study, emotion, Emotions, Facial Expression, facial expressions, Facial Recognition, female, human, Humans, male, physiology, Psychophysics, simulation, Spatial frequencies, Young Adult
@article{charbonneauFacialExpressionCategorization2025,
title = {Facial expression categorization predominantly relies on mid-spatial frequencies},
author = {I. Charbonneau and J. Duncan and C. Blais and J. Guérette and M. -P. Plouffe-Demers and F. Smith and D. Fiset},
url = {https://www.scopus.com/pages/publications/105003427898?origin=resultslist},
doi = {10.1016/j.visres.2025.108611},
issn = {00426989 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Vision Research},
volume = {231},
publisher = {Elsevier Ltd},
abstract = {Facial expressions are crucial in human communication. Recent decades have seen growing interest in understanding the role of spatial frequencies (SFs) in emotion perception in others. While some studies have suggested a preferential treatment of low versus high SFs, the optimal SFs for recognizing basic facial expressions remain elusive. This study, conducted on Western participants, addresses this gap using two complementary methods: a data-driven method (Exp. 1) without arbitrary SF cut-offs, and a more naturalistic method (Exp. 2) simulating variations in viewing distance. Results generally showed a preponderant role of low over high SFs, but particularly stress that facial expression categorization mostly relies on mid-range SF content (i.e. ∼6–13 cycles per face), often overlooked in previous studies. Optimal performance was observed at short to medium viewing distances (1.2–2.4 m), declining sharply with increased distance, precisely when mid-range SFs were no longer available. Additionally, our data suggest variations in SF tuning profiles across basic facial expressions and nuanced contributions from low and mid SFs in facial expression processing. Most importantly, it suggests that any method that removes mid-SF content has the downfall of offering an incomplete account of SFs diagnosticity for facial expression recognition. © 2025 The Authors},
keywords = {adult, article, Bubbles, Classification, controlled study, emotion, Emotions, Facial Expression, facial expressions, Facial Recognition, female, human, Humans, male, physiology, Psychophysics, simulation, Spatial frequencies, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Facial expressions are crucial in human communication. Recent decades have seen growing interest in understanding the role of spatial frequencies (SFs) in emotion perception in others. While some studies have suggested a preferential treatment of low versus high SFs, the optimal SFs for recognizing basic facial expressions remain elusive. This study, conducted on Western participants, addresses this gap using two complementary methods: a data-driven method (Exp. 1) without arbitrary SF cut-offs, and a more naturalistic method (Exp. 2) simulating variations in viewing distance. Results generally showed a preponderant role of low over high SFs, but particularly stress that facial expression categorization mostly relies on mid-range SF content (i.e. ∼6–13 cycles per face), often overlooked in previous studies. Optimal performance was observed at short to medium viewing distances (1.2–2.4 m), declining sharply with increased distance, precisely when mid-range SFs were no longer available. Additionally, our data suggest variations in SF tuning profiles across basic facial expressions and nuanced contributions from low and mid SFs in facial expression processing. Most importantly, it suggests that any method that removes mid-SF content has the downfall of offering an incomplete account of SFs diagnosticity for facial expression recognition. © 2025 The Authors
3.
Charbonneau, I.; Duncan, J.; Blais, C.; Guérette, J.; Plouffe-Demers, M. -P.; Smith, F.; Fiset, D.
Facial expression categorization predominantly relies on mid-spatial frequencies Journal Article
In: Vision Research, vol. 231, 2025, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Bubbles, Classification, controlled study, emotion, Emotions, Facial Expression, facial expressions, Facial Recognition, female, human, Humans, male, physiology, Psychophysics, simulation, Spatial frequencies, Young Adult
@article{charbonneauFacialExpressionCategorization2025a,
title = {Facial expression categorization predominantly relies on mid-spatial frequencies},
author = {I. Charbonneau and J. Duncan and C. Blais and J. Guérette and M. -P. Plouffe-Demers and F. Smith and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003427898&doi=10.1016%2Fj.visres.2025.108611&partnerID=40&md5=508d315d8092a9142c2d82f1b774cfdb},
doi = {10.1016/j.visres.2025.108611},
issn = {00426989 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Vision Research},
volume = {231},
abstract = {Facial expressions are crucial in human communication. Recent decades have seen growing interest in understanding the role of spatial frequencies (SFs) in emotion perception in others. While some studies have suggested a preferential treatment of low versus high SFs, the optimal SFs for recognizing basic facial expressions remain elusive. This study, conducted on Western participants, addresses this gap using two complementary methods: a data-driven method (Exp. 1) without arbitrary SF cut-offs, and a more naturalistic method (Exp. 2) simulating variations in viewing distance. Results generally showed a preponderant role of low over high SFs, but particularly stress that facial expression categorization mostly relies on mid-range SF content (i.e. ∼6–13 cycles per face), often overlooked in previous studies. Optimal performance was observed at short to medium viewing distances (1.2–2.4 m), declining sharply with increased distance, precisely when mid-range SFs were no longer available. Additionally, our data suggest variations in SF tuning profiles across basic facial expressions and nuanced contributions from low and mid SFs in facial expression processing. Most importantly, it suggests that any method that removes mid-SF content has the downfall of offering an incomplete account of SFs diagnosticity for facial expression recognition. © 2025 The Authors},
keywords = {adult, article, Bubbles, Classification, controlled study, emotion, Emotions, Facial Expression, facial expressions, Facial Recognition, female, human, Humans, male, physiology, Psychophysics, simulation, Spatial frequencies, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Facial expressions are crucial in human communication. Recent decades have seen growing interest in understanding the role of spatial frequencies (SFs) in emotion perception in others. While some studies have suggested a preferential treatment of low versus high SFs, the optimal SFs for recognizing basic facial expressions remain elusive. This study, conducted on Western participants, addresses this gap using two complementary methods: a data-driven method (Exp. 1) without arbitrary SF cut-offs, and a more naturalistic method (Exp. 2) simulating variations in viewing distance. Results generally showed a preponderant role of low over high SFs, but particularly stress that facial expression categorization mostly relies on mid-range SF content (i.e. ∼6–13 cycles per face), often overlooked in previous studies. Optimal performance was observed at short to medium viewing distances (1.2–2.4 m), declining sharply with increased distance, precisely when mid-range SFs were no longer available. Additionally, our data suggest variations in SF tuning profiles across basic facial expressions and nuanced contributions from low and mid SFs in facial expression processing. Most importantly, it suggests that any method that removes mid-SF content has the downfall of offering an incomplete account of SFs diagnosticity for facial expression recognition. © 2025 The Authors
4.
Amirkhani, D.; Allili, M. S.; Hebbache, L.; Hammouche, N.; Lapointe, J.
Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, pp. 1–23, 2024, ISSN: 15249050, (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Annotation, Annotations, Bridges, Classification, Concrete, Concrete bridge defect, Concrete bridge defects, Concrete bridges, Concrete defects, Concretes, Deep learning, Defect classification, Defect detection, Defects, Detection, Inspection, Reviews, Segmentation, Taxonomies, Visualization
@article{amirkhani_visual_2024,
title = {Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review},
author = {D. Amirkhani and M. S. Allili and L. Hebbache and N. Hammouche and J. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186994244&doi=10.1109%2fTITS.2024.3365296&partnerID=40&md5=a9228252d620ad6d444cc395296ebac2},
doi = {10.1109/TITS.2024.3365296},
issn = {15249050},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
pages = {1–23},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Visual inspection is an important process for maintaining bridges in road transportation systems, and preventing catastrophic events and tragedies. In this process, accurate and automatic concrete defect classification and detection are major components to ensure early identification of any issue that can compromise the bridge safety and integrity. While a tremendous body of research has been proposed in the last decades for addressing these problems, the advent of deep learning unleashed huge opportunities for building more accurate and efficient methods. Our aim in this survey is to study the recent progress of vision-based concrete bridge defect classification and detection in the deep learning era. Our review encompasses major aspects underlying typical frameworks, which include concrete defect taxonomy, public datasets and evaluation metrics. We provide also a taxonomy of deep-learning-based classification and detection algorithms with a detailed discussion of their advantages and limitations. We also benchmark baseline models for classification and detection, using two popular datasets. We finally discuss important challenges of concrete defect classification and detection, and promising research avenues to build better models and integrate them in real-world visual inspection systems, which warrant further scientific investigation. IEEE},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Annotation, Annotations, Bridges, Classification, Concrete, Concrete bridge defect, Concrete bridge defects, Concrete bridges, Concrete defects, Concretes, Deep learning, Defect classification, Defect detection, Defects, Detection, Inspection, Reviews, Segmentation, Taxonomies, Visualization},
pubstate = {published},
tppubtype = {article}
}
Visual inspection is an important process for maintaining bridges in road transportation systems, and preventing catastrophic events and tragedies. In this process, accurate and automatic concrete defect classification and detection are major components to ensure early identification of any issue that can compromise the bridge safety and integrity. While a tremendous body of research has been proposed in the last decades for addressing these problems, the advent of deep learning unleashed huge opportunities for building more accurate and efficient methods. Our aim in this survey is to study the recent progress of vision-based concrete bridge defect classification and detection in the deep learning era. Our review encompasses major aspects underlying typical frameworks, which include concrete defect taxonomy, public datasets and evaluation metrics. We provide also a taxonomy of deep-learning-based classification and detection algorithms with a detailed discussion of their advantages and limitations. We also benchmark baseline models for classification and detection, using two popular datasets. We finally discuss important challenges of concrete defect classification and detection, and promising research avenues to build better models and integrate them in real-world visual inspection systems, which warrant further scientific investigation. IEEE
5.
Amirkhani, D.; Allili, M. S.; Hebbache, L.; Hammouche, N.; Lapointe, J.
Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, pp. 1–23, 2024, ISSN: 15249050.
Abstract | Links | BibTeX | Tags: Annotation, Annotations, Bridges, Classification, Concrete, Concrete bridge defect, Concrete bridge defects, Concrete bridges, Concrete defects, Concretes, Deep learning, Defect classification, Defect detection, Defects, Detection, Inspection, Reviews, Segmentation, Taxonomies, Visualization
@article{amirkhaniVisualConcreteBridge2024,
title = {Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review},
author = {D. Amirkhani and M. S. Allili and L. Hebbache and N. Hammouche and J. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186994244&doi=10.1109%2fTITS.2024.3365296&partnerID=40&md5=a9228252d620ad6d444cc395296ebac2},
doi = {10.1109/TITS.2024.3365296},
issn = {15249050},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
pages = {1–23},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Visual inspection is an important process for maintaining bridges in road transportation systems, and preventing catastrophic events and tragedies. In this process, accurate and automatic concrete defect classification and detection are major components to ensure early identification of any issue that can compromise the bridge safety and integrity. While a tremendous body of research has been proposed in the last decades for addressing these problems, the advent of deep learning unleashed huge opportunities for building more accurate and efficient methods. Our aim in this survey is to study the recent progress of vision-based concrete bridge defect classification and detection in the deep learning era. Our review encompasses major aspects underlying typical frameworks, which include concrete defect taxonomy, public datasets and evaluation metrics. We provide also a taxonomy of deep-learning-based classification and detection algorithms with a detailed discussion of their advantages and limitations. We also benchmark baseline models for classification and detection, using two popular datasets. We finally discuss important challenges of concrete defect classification and detection, and promising research avenues to build better models and integrate them in real-world visual inspection systems, which warrant further scientific investigation. IEEE},
keywords = {Annotation, Annotations, Bridges, Classification, Concrete, Concrete bridge defect, Concrete bridge defects, Concrete bridges, Concrete defects, Concretes, Deep learning, Defect classification, Defect detection, Defects, Detection, Inspection, Reviews, Segmentation, Taxonomies, Visualization},
pubstate = {published},
tppubtype = {article}
}
Visual inspection is an important process for maintaining bridges in road transportation systems, and preventing catastrophic events and tragedies. In this process, accurate and automatic concrete defect classification and detection are major components to ensure early identification of any issue that can compromise the bridge safety and integrity. While a tremendous body of research has been proposed in the last decades for addressing these problems, the advent of deep learning unleashed huge opportunities for building more accurate and efficient methods. Our aim in this survey is to study the recent progress of vision-based concrete bridge defect classification and detection in the deep learning era. Our review encompasses major aspects underlying typical frameworks, which include concrete defect taxonomy, public datasets and evaluation metrics. We provide also a taxonomy of deep-learning-based classification and detection algorithms with a detailed discussion of their advantages and limitations. We also benchmark baseline models for classification and detection, using two popular datasets. We finally discuss important challenges of concrete defect classification and detection, and promising research avenues to build better models and integrate them in real-world visual inspection systems, which warrant further scientific investigation. IEEE
6.
Cote, S. S. -P.; Paquette, G. R.; Neveu, S. -M.; Chartier, S.; Labbe, D. R.; Renaud, P.
Combining electroencephalography with plethysmography for classification of deviant sexual preferences. Proceedings Article
In: Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-172819556-8 (ISBN), (Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF).
Abstract | Links | BibTeX | Tags: Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction
@inproceedings{cote_combining_2021,
title = {Combining electroencephalography with plethysmography for classification of deviant sexual preferences.},
author = {S. S. -P. Cote and G. R. Paquette and S. -M. Neveu and S. Chartier and D. R. Labbe and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113855965&doi=10.1109%2fIWBF50991.2021.9465078&partnerID=40&md5=b545b2a6d22e32115ac179399188960e},
doi = {10.1109/IWBF50991.2021.9465078},
isbn = {978-172819556-8 (ISBN)},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.},
note = {Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF},
keywords = {Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction},
pubstate = {published},
tppubtype = {inproceedings}
}
Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.
7.
Charbonneau, I.; Guérette, J.; Cormier, S.; Blais, C.; Lalonde-Beaudoin, G.; Smith, F. W.; Fiset, D.
The role of spatial frequencies for facial pain categorization Journal Article
In: Scientific Reports, vol. 11, no. 1, 2021, ISSN: 20452322, (Publisher: Nature Research).
Abstract | Links | BibTeX | Tags: Adolescent, adult, Classification, Distance Perception, emotion, Emotions, Face, face pain, Facial Expression, Facial Pain, Facial Recognition, female, human, Humans, Knowledge, male, Normal Distribution, Pattern Recognition, procedures, psychology, Psychophysics, recognition, reproducibility, Reproducibility of Results, Visual, Young Adult
@article{charbonneau_role_2021,
title = {The role of spatial frequencies for facial pain categorization},
author = {I. Charbonneau and J. Guérette and S. Cormier and C. Blais and G. Lalonde-Beaudoin and F. W. Smith and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111138273&doi=10.1038%2fs41598-021-93776-7&partnerID=40&md5=d759d0218de65fce371bb51d7f2593d8},
doi = {10.1038/s41598-021-93776-7},
issn = {20452322},
year = {2021},
date = {2021-01-01},
journal = {Scientific Reports},
volume = {11},
number = {1},
publisher = {Nature Research},
abstract = {Studies on low-level visual information underlying pain categorization have led to inconsistent findings. Some show an advantage for low spatial frequency information (SFs) and others a preponderance of mid SFs. This study aims to clarify this gap in knowledge since these results have different theoretical and practical implications, such as how far away an observer can be in order to categorize pain. This study addresses this question by using two complementary methods: a data-driven method without a priori expectations about the most useful SFs for pain recognition and a more ecological method that simulates the distance of stimuli presentation. We reveal a broad range of important SFs for pain recognition starting from low to relatively high SFs and showed that performance is optimal in a short to medium distance (1.2–4.8 m) but declines significantly when mid SFs are no longer available. This study reconciles previous results that show an advantage of LSFs over HSFs when using arbitrary cutoffs, but above all reveal the prominent role of mid-SFs for pain recognition across two complementary experimental tasks. © 2021, The Author(s).},
note = {Publisher: Nature Research},
keywords = {Adolescent, adult, Classification, Distance Perception, emotion, Emotions, Face, face pain, Facial Expression, Facial Pain, Facial Recognition, female, human, Humans, Knowledge, male, Normal Distribution, Pattern Recognition, procedures, psychology, Psychophysics, recognition, reproducibility, Reproducibility of Results, Visual, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Studies on low-level visual information underlying pain categorization have led to inconsistent findings. Some show an advantage for low spatial frequency information (SFs) and others a preponderance of mid SFs. This study aims to clarify this gap in knowledge since these results have different theoretical and practical implications, such as how far away an observer can be in order to categorize pain. This study addresses this question by using two complementary methods: a data-driven method without a priori expectations about the most useful SFs for pain recognition and a more ecological method that simulates the distance of stimuli presentation. We reveal a broad range of important SFs for pain recognition starting from low to relatively high SFs and showed that performance is optimal in a short to medium distance (1.2–4.8 m) but declines significantly when mid SFs are no longer available. This study reconciles previous results that show an advantage of LSFs over HSFs when using arbitrary cutoffs, but above all reveal the prominent role of mid-SFs for pain recognition across two complementary experimental tasks. © 2021, The Author(s).
8.
Cote, S. S. -P.; Paquette, G. R.; Neveu, S. -M.; Chartier, S.; Labbe, D. R.; Renaud, P.
Combining electroencephalography with plethysmography for classification of deviant sexual preferences. Proceedings Article
In: Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-172819556-8 (ISBN), (Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF).
Abstract | Links | BibTeX | Tags: Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction
@inproceedings{coteCombiningElectroencephalographyPlethysmography2021,
title = {Combining electroencephalography with plethysmography for classification of deviant sexual preferences.},
author = {S. S. -P. Cote and G. R. Paquette and S. -M. Neveu and S. Chartier and D. R. Labbe and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113855965&doi=10.1109%2fIWBF50991.2021.9465078&partnerID=40&md5=b545b2a6d22e32115ac179399188960e},
doi = {10.1109/IWBF50991.2021.9465078},
isbn = {978-172819556-8 (ISBN)},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.},
note = {Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF},
keywords = {Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction},
pubstate = {published},
tppubtype = {inproceedings}
}
Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.
9.
Saidani, N.; Adi, K.; Allili, M. S.
A semantic-based classification approach for an enhanced spam detection Journal Article
In: Computers and Security, vol. 94, 2020, ISSN: 01674048 (ISSN), (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: Classification, Classification approach, Conceptual views, Domain-specific analysis, Electronic mail, Email content, Multilevel analysis, Semantic analysis, Semantic content, Semantic features, Semantic levels, Semantics, Spam detection
@article{saidani_semantic-based_2020,
title = {A semantic-based classification approach for an enhanced spam detection},
author = {N. Saidani and K. Adi and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084283123&doi=10.1016%2fj.cose.2020.101716&partnerID=40&md5=539ac0fc0a7144fe983f514175a138e2},
doi = {10.1016/j.cose.2020.101716},
issn = {01674048 (ISSN)},
year = {2020},
date = {2020-01-01},
journal = {Computers and Security},
volume = {94},
publisher = {Elsevier Ltd},
abstract = {In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results. © 2020},
note = {Publisher: Elsevier Ltd},
keywords = {Classification, Classification approach, Conceptual views, Domain-specific analysis, Electronic mail, Email content, Multilevel analysis, Semantic analysis, Semantic content, Semantic features, Semantic levels, Semantics, Spam detection},
pubstate = {published},
tppubtype = {article}
}
In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results. © 2020
10.
Saidani, N.; Adi, K.; Allili, M. S.
A semantic-based classification approach for an enhanced spam detection Journal Article
In: Computers and Security, vol. 94, 2020, ISSN: 01674048 (ISSN).
Abstract | Links | BibTeX | Tags: Classification, Classification approach, Conceptual views, Domain-specific analysis, Electronic mail, Email content, Multilevel analysis, Semantic analysis, Semantic content, Semantic features, Semantic levels, Semantics, Spam detection
@article{saidaniSemanticbasedClassificationApproach2020,
title = {A semantic-based classification approach for an enhanced spam detection},
author = {N. Saidani and K. Adi and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084283123&doi=10.1016%2fj.cose.2020.101716&partnerID=40&md5=539ac0fc0a7144fe983f514175a138e2},
doi = {10.1016/j.cose.2020.101716},
issn = {01674048 (ISSN)},
year = {2020},
date = {2020-01-01},
journal = {Computers and Security},
volume = {94},
publisher = {Elsevier Ltd},
abstract = {In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results. © 2020},
keywords = {Classification, Classification approach, Conceptual views, Domain-specific analysis, Electronic mail, Email content, Multilevel analysis, Semantic analysis, Semantic content, Semantic features, Semantic levels, Semantics, Spam detection},
pubstate = {published},
tppubtype = {article}
}
In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results. © 2020