Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Bouafia, Y.; Allili, M. S.; Hebbache, L.; Guezouli, L.
SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions Journal Article
In: Signal Processing: Image Communication, vol. 130, 2025, ISSN: 09235965 (ISSN), (Publisher: Elsevier B.V.).
Abstract | Links | BibTeX | Tags: Condition, Deep learning, face recognition, Hazy weather, Human detection, Knowledge distillation, Learning models, Lightweight Retinanet, Outdoor scenes, Personal safety, Personal security, Safety and securities
@article{bouafia_ses-renet_2025,
title = {SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions},
author = {Y. Bouafia and M. S. Allili and L. Hebbache and L. Guezouli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208562795&doi=10.1016%2fj.image.2024.117223&partnerID=40&md5=322b79f8d78045395efcabcbf86c6e1c},
doi = {10.1016/j.image.2024.117223},
issn = {09235965 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Signal Processing: Image Communication},
volume = {130},
publisher = {Elsevier B.V.},
abstract = {Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024},
note = {Publisher: Elsevier B.V.},
keywords = {Condition, Deep learning, face recognition, Hazy weather, Human detection, Knowledge distillation, Learning models, Lightweight Retinanet, Outdoor scenes, Personal safety, Personal security, Safety and securities},
pubstate = {published},
tppubtype = {article}
}
Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024
2.
Audette, P. -L.; Côté, L.; Blais, C.; Duncan, J.; Gingras, F.; Fiset, D.
Part-based processing, but not holistic processing, predicts individual differences in face recognition abilities Journal Article
In: Cognition, vol. 256, 2025, ISSN: 00100277 (ISSN), (Publisher: Elsevier B.V.).
Abstract | Links | BibTeX | Tags: Adolescent, adult, article, face perception, face recognition, Facial Recognition, female, human, human experiment, Humans, Individual differences, Individuality, Integration efficiency, male, multiple linear regression analysis, physiology, psychology, Psychophysics, recognition, Young Adult
@article{audette_part-based_2025,
title = {Part-based processing, but not holistic processing, predicts individual differences in face recognition abilities},
author = {P. -L. Audette and L. Côté and C. Blais and J. Duncan and F. Gingras and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213575434&doi=10.1016%2fj.cognition.2024.106057&partnerID=40&md5=135d2ba1bdf18648b57db0d3a93d0628},
doi = {10.1016/j.cognition.2024.106057},
issn = {00100277 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Cognition},
volume = {256},
publisher = {Elsevier B.V.},
abstract = {This study aimed to assess the roles of part-based and holistic processing for face processing ability (FPA). A psychophysical paradigm in which the efficiency at recognizing isolated or combined facial parts was used (N = 64), and holistic processing was defined as the perceptual integration from multiple parts. FPA and object processing ability were measured using a battery of tasks. A multiple linear regression including three predictors, namely perceptual integration, part-based efficiency, and object processing, explained 40 % of the variance in FPA. Most importantly, our results reveal a strong predictive relationship between part-based efficiency and FPA, a small predictive relationship between object processing ability and FPA, and no predictive relationship between perceptual integration and FPA. This result was obtained despite considerable variance in perceptual integration skills–with some participants exhibiting a highly efficient integration. These results indicate that part-based processing plays a pivotal role in FPA, whereas holistic processing does not. © 2024 The Authors},
note = {Publisher: Elsevier B.V.},
keywords = {Adolescent, adult, article, face perception, face recognition, Facial Recognition, female, human, human experiment, Humans, Individual differences, Individuality, Integration efficiency, male, multiple linear regression analysis, physiology, psychology, Psychophysics, recognition, Young Adult},
pubstate = {published},
tppubtype = {article}
}
This study aimed to assess the roles of part-based and holistic processing for face processing ability (FPA). A psychophysical paradigm in which the efficiency at recognizing isolated or combined facial parts was used (N = 64), and holistic processing was defined as the perceptual integration from multiple parts. FPA and object processing ability were measured using a battery of tasks. A multiple linear regression including three predictors, namely perceptual integration, part-based efficiency, and object processing, explained 40 % of the variance in FPA. Most importantly, our results reveal a strong predictive relationship between part-based efficiency and FPA, a small predictive relationship between object processing ability and FPA, and no predictive relationship between perceptual integration and FPA. This result was obtained despite considerable variance in perceptual integration skills–with some participants exhibiting a highly efficient integration. These results indicate that part-based processing plays a pivotal role in FPA, whereas holistic processing does not. © 2024 The Authors
3.
Bouafia, Y.; Allili, M. S.; Hebbache, L.; Guezouli, L.
SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions Journal Article
In: Signal Processing: Image Communication, vol. 130, 2025, ISSN: 09235965 (ISSN).
Abstract | Links | BibTeX | Tags: Condition, Deep learning, face recognition, Hazy weather, Human detection, Knowledge distillation, Learning models, Lightweight Retinanet, Outdoor scenes, Personal safety, Personal security, Safety and securities
@article{bouafiaSESReNetLightweightDeep2025a,
title = {SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions},
author = {Y. Bouafia and M. S. Allili and L. Hebbache and L. Guezouli},
url = {https://www.scopus.com/pages/publications/85208562795?origin=resultslist},
doi = {10.1016/j.image.2024.117223},
issn = {09235965 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Signal Processing: Image Communication},
volume = {130},
publisher = {Elsevier B.V.},
abstract = {Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024},
keywords = {Condition, Deep learning, face recognition, Hazy weather, Human detection, Knowledge distillation, Learning models, Lightweight Retinanet, Outdoor scenes, Personal safety, Personal security, Safety and securities},
pubstate = {published},
tppubtype = {article}
}
Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024
4.
Audette, P. -L.; Côté, L.; Blais, C.; Duncan, J.; Gingras, F.; Fiset, D.
Part-based processing, but not holistic processing, predicts individual differences in face recognition abilities Journal Article
In: Cognition, vol. 256, 2025, ISSN: 00100277 (ISSN).
Abstract | Links | BibTeX | Tags: Adolescent, adult, article, face perception, face recognition, Facial Recognition, female, human, human experiment, Humans, Individual differences, Individuality, Integration efficiency, male, multiple linear regression analysis, physiology, psychology, Psychophysics, recognition, Young Adult
@article{audettePartbasedProcessingNot2025a,
title = {Part-based processing, but not holistic processing, predicts individual differences in face recognition abilities},
author = {P. -L. Audette and L. Côté and C. Blais and J. Duncan and F. Gingras and D. Fiset},
url = {https://www.scopus.com/pages/publications/85213575434?origin=resultslist},
doi = {10.1016/j.cognition.2024.106057},
issn = {00100277 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Cognition},
volume = {256},
publisher = {Elsevier B.V.},
abstract = {This study aimed to assess the roles of part-based and holistic processing for face processing ability (FPA). A psychophysical paradigm in which the efficiency at recognizing isolated or combined facial parts was used (N = 64), and holistic processing was defined as the perceptual integration from multiple parts. FPA and object processing ability were measured using a battery of tasks. A multiple linear regression including three predictors, namely perceptual integration, part-based efficiency, and object processing, explained 40 % of the variance in FPA. Most importantly, our results reveal a strong predictive relationship between part-based efficiency and FPA, a small predictive relationship between object processing ability and FPA, and no predictive relationship between perceptual integration and FPA. This result was obtained despite considerable variance in perceptual integration skills–with some participants exhibiting a highly efficient integration. These results indicate that part-based processing plays a pivotal role in FPA, whereas holistic processing does not. © 2024 The Authors},
keywords = {Adolescent, adult, article, face perception, face recognition, Facial Recognition, female, human, human experiment, Humans, Individual differences, Individuality, Integration efficiency, male, multiple linear regression analysis, physiology, psychology, Psychophysics, recognition, Young Adult},
pubstate = {published},
tppubtype = {article}
}
This study aimed to assess the roles of part-based and holistic processing for face processing ability (FPA). A psychophysical paradigm in which the efficiency at recognizing isolated or combined facial parts was used (N = 64), and holistic processing was defined as the perceptual integration from multiple parts. FPA and object processing ability were measured using a battery of tasks. A multiple linear regression including three predictors, namely perceptual integration, part-based efficiency, and object processing, explained 40 % of the variance in FPA. Most importantly, our results reveal a strong predictive relationship between part-based efficiency and FPA, a small predictive relationship between object processing ability and FPA, and no predictive relationship between perceptual integration and FPA. This result was obtained despite considerable variance in perceptual integration skills–with some participants exhibiting a highly efficient integration. These results indicate that part-based processing plays a pivotal role in FPA, whereas holistic processing does not. © 2024 The Authors
5.
Bouafia, Y.; Allili, M. S.; Hebbache, L.; Guezouli, L.
SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions Journal Article
In: Signal Processing: Image Communication, vol. 130, 2025, ISSN: 09235965 (ISSN).
Abstract | Links | BibTeX | Tags: Condition, Deep learning, face recognition, Hazy weather, Human detection, Knowledge distillation, Learning models, Lightweight Retinanet, Outdoor scenes, Personal safety, Personal security, Safety and securities
@article{bouafiaSESReNetLightweightDeep2025,
title = {SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions},
author = {Y. Bouafia and M. S. Allili and L. Hebbache and L. Guezouli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208562795&doi=10.1016%2fj.image.2024.117223&partnerID=40&md5=322b79f8d78045395efcabcbf86c6e1c},
doi = {10.1016/j.image.2024.117223},
issn = {09235965 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Signal Processing: Image Communication},
volume = {130},
publisher = {Elsevier B.V.},
abstract = {Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024},
keywords = {Condition, Deep learning, face recognition, Hazy weather, Human detection, Knowledge distillation, Learning models, Lightweight Retinanet, Outdoor scenes, Personal safety, Personal security, Safety and securities},
pubstate = {published},
tppubtype = {article}
}
Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024
6.
Audette, P. -L.; Côté, L.; Blais, C.; Duncan, J.; Gingras, F.; Fiset, D.
Part-based processing, but not holistic processing, predicts individual differences in face recognition abilities Journal Article
In: Cognition, vol. 256, 2025, ISSN: 00100277 (ISSN).
Abstract | Links | BibTeX | Tags: Adolescent, adult, article, face perception, face recognition, Facial Recognition, female, human, human experiment, Humans, Individual differences, Individuality, Integration efficiency, male, multiple linear regression analysis, physiology, psychology, Psychophysics, recognition, Young Adult
@article{audettePartbasedProcessingNot2025,
title = {Part-based processing, but not holistic processing, predicts individual differences in face recognition abilities},
author = {P. -L. Audette and L. Côté and C. Blais and J. Duncan and F. Gingras and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213575434&doi=10.1016%2fj.cognition.2024.106057&partnerID=40&md5=135d2ba1bdf18648b57db0d3a93d0628},
doi = {10.1016/j.cognition.2024.106057},
issn = {00100277 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Cognition},
volume = {256},
publisher = {Elsevier B.V.},
abstract = {This study aimed to assess the roles of part-based and holistic processing for face processing ability (FPA). A psychophysical paradigm in which the efficiency at recognizing isolated or combined facial parts was used (N = 64), and holistic processing was defined as the perceptual integration from multiple parts. FPA and object processing ability were measured using a battery of tasks. A multiple linear regression including three predictors, namely perceptual integration, part-based efficiency, and object processing, explained 40 % of the variance in FPA. Most importantly, our results reveal a strong predictive relationship between part-based efficiency and FPA, a small predictive relationship between object processing ability and FPA, and no predictive relationship between perceptual integration and FPA. This result was obtained despite considerable variance in perceptual integration skills–with some participants exhibiting a highly efficient integration. These results indicate that part-based processing plays a pivotal role in FPA, whereas holistic processing does not. © 2024 The Authors},
keywords = {Adolescent, adult, article, face perception, face recognition, Facial Recognition, female, human, human experiment, Humans, Individual differences, Individuality, Integration efficiency, male, multiple linear regression analysis, physiology, psychology, Psychophysics, recognition, Young Adult},
pubstate = {published},
tppubtype = {article}
}
This study aimed to assess the roles of part-based and holistic processing for face processing ability (FPA). A psychophysical paradigm in which the efficiency at recognizing isolated or combined facial parts was used (N = 64), and holistic processing was defined as the perceptual integration from multiple parts. FPA and object processing ability were measured using a battery of tasks. A multiple linear regression including three predictors, namely perceptual integration, part-based efficiency, and object processing, explained 40 % of the variance in FPA. Most importantly, our results reveal a strong predictive relationship between part-based efficiency and FPA, a small predictive relationship between object processing ability and FPA, and no predictive relationship between perceptual integration and FPA. This result was obtained despite considerable variance in perceptual integration skills–with some participants exhibiting a highly efficient integration. These results indicate that part-based processing plays a pivotal role in FPA, whereas holistic processing does not. © 2024 The Authors
7.
Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.; Guimond, S.
Prediction of Continuous Emotional Measures through Physiological and Visual Data † Journal Article
In: Sensors, vol. 23, no. 12, pp. 17–21, 2023, ISSN: 14248220, (Publisher: Interactive Media Institute).
Abstract | Links | BibTeX | Tags: Affect recognition, Affective state, Arousal, Data-source, Deep learning, Electrocardiography, emotion, Emotion Recognition, Emotions, face recognition, Faces detection, Forecasting, human, Humans, Images processing, Learning systems, Machine learning, Machine-learning, mental disease, Mental Disorders, Physiological data, physiology, Signal-processing, Statistical tests, Video recording, Virtual-reality environment
@article{joudeh_prediction_2023-1,
title = {Prediction of Continuous Emotional Measures through Physiological and Visual Data †},
author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard and S. Guimond},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163943735&doi=10.3390%2fs23125613&partnerID=40&md5=5e970f0d8c5790b85d8d77a9f3f52a2d},
doi = {10.3390/s23125613},
issn = {14248220},
year = {2023},
date = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {12},
pages = {17–21},
publisher = {MDPI},
abstract = {The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. © 2023 by the authors.},
note = {Publisher: Interactive Media Institute},
keywords = {Affect recognition, Affective state, Arousal, Data-source, Deep learning, Electrocardiography, emotion, Emotion Recognition, Emotions, face recognition, Faces detection, Forecasting, human, Humans, Images processing, Learning systems, Machine learning, Machine-learning, mental disease, Mental Disorders, Physiological data, physiology, Signal-processing, Statistical tests, Video recording, Virtual-reality environment},
pubstate = {published},
tppubtype = {article}
}
The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. © 2023 by the authors.
8.
Royer, J.; Blais, C.; Barnabé-Lortie, V.; Carré, M.; Leclerc, J.; Fiset, D.
Efficient visual information for unfamiliar face matching despite viewpoint variations: It's not in the eyes! Journal Article
In: Vision Research, vol. 123, pp. 33–40, 2016, ISSN: 00426989 (ISSN), (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: accuracy, adult, article, association, attention, Bubbles, Evoked Potentials, eye fixation, Face, face profile, face recognition, Facial Recognition, facies, female, Fixation, human, human experiment, Humans, Image analysis, Individual differences, male, Ocular, Pattern Recognition, Photic Stimulation, photostimulation, physiology, priority journal, procedures, Psychophysics, recognition, Recognition (Psychology), regression analysis, task performance, unfamiliar face matching, viewpoint variation, Viewpoint variations, Visual, visual discrimination, visual evoked potential, visual information, visual memory, visual stimulation, visual system parameters, Young Adult
@article{royer_efficient_2016,
title = {Efficient visual information for unfamiliar face matching despite viewpoint variations: It's not in the eyes!},
author = {J. Royer and C. Blais and V. Barnabé-Lortie and M. Carré and J. Leclerc and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968779426&doi=10.1016%2fj.visres.2016.04.004&partnerID=40&md5=4c63f6eea279f7322c9af23ae9ed22c1},
doi = {10.1016/j.visres.2016.04.004},
issn = {00426989 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {Vision Research},
volume = {123},
pages = {33–40},
publisher = {Elsevier Ltd},
abstract = {Faces are encountered in highly diverse angles in real-world settings. Despite this considerable diversity, most individuals are able to easily recognize familiar faces. The vast majority of studies in the field of face recognition have nonetheless focused almost exclusively on frontal views of faces. Indeed, a number of authors have investigated the diagnostic facial features for the recognition of frontal views of faces previously encoded in this same view. However, the nature of the information useful for identity matching when the encoded face and test face differ in viewing angle remains mostly unexplored. The present study addresses this issue using individual differences and bubbles, a method that pinpoints the facial features effectively used in a visual categorization task. Our results indicate that the use of features located in the center of the face, the lower left portion of the nose area and the center of the mouth, are significantly associated with individual efficiency to generalize a face's identity across different viewpoints. However, as faces become more familiar, the reliance on this area decreases, while the diagnosticity of the eye region increases. This suggests that a certain distinction can be made between the visual mechanisms subtending viewpoint invariance and face recognition in the case of unfamiliar face identification. Our results further support the idea that the eye area may only come into play when the face stimulus is particularly familiar to the observer. © 2016 Elsevier Ltd.},
note = {Publisher: Elsevier Ltd},
keywords = {accuracy, adult, article, association, attention, Bubbles, Evoked Potentials, eye fixation, Face, face profile, face recognition, Facial Recognition, facies, female, Fixation, human, human experiment, Humans, Image analysis, Individual differences, male, Ocular, Pattern Recognition, Photic Stimulation, photostimulation, physiology, priority journal, procedures, Psychophysics, recognition, Recognition (Psychology), regression analysis, task performance, unfamiliar face matching, viewpoint variation, Viewpoint variations, Visual, visual discrimination, visual evoked potential, visual information, visual memory, visual stimulation, visual system parameters, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Faces are encountered in highly diverse angles in real-world settings. Despite this considerable diversity, most individuals are able to easily recognize familiar faces. The vast majority of studies in the field of face recognition have nonetheless focused almost exclusively on frontal views of faces. Indeed, a number of authors have investigated the diagnostic facial features for the recognition of frontal views of faces previously encoded in this same view. However, the nature of the information useful for identity matching when the encoded face and test face differ in viewing angle remains mostly unexplored. The present study addresses this issue using individual differences and bubbles, a method that pinpoints the facial features effectively used in a visual categorization task. Our results indicate that the use of features located in the center of the face, the lower left portion of the nose area and the center of the mouth, are significantly associated with individual efficiency to generalize a face's identity across different viewpoints. However, as faces become more familiar, the reliance on this area decreases, while the diagnosticity of the eye region increases. This suggests that a certain distinction can be made between the visual mechanisms subtending viewpoint invariance and face recognition in the case of unfamiliar face identification. Our results further support the idea that the eye area may only come into play when the face stimulus is particularly familiar to the observer. © 2016 Elsevier Ltd.
9.
Royer, J.; Blais, C.; Barnabé-Lortie, V.; Carré, M.; Leclerc, J.; Fiset, D.
Efficient visual information for unfamiliar face matching despite viewpoint variations: It's not in the eyes! Journal Article
In: Vision Research, vol. 123, pp. 33–40, 2016, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: accuracy, adult, article, association, attention, Bubbles, Evoked Potentials, eye fixation, Face, face profile, face recognition, Facial Recognition, facies, female, Fixation, human, human experiment, Humans, Image analysis, Individual differences, male, Ocular, Pattern Recognition, Photic Stimulation, photostimulation, physiology, priority journal, procedures, Psychophysics, recognition, Recognition (Psychology), regression analysis, task performance, unfamiliar face matching, viewpoint variation, Viewpoint variations, Visual, visual discrimination, visual evoked potential, visual information, visual memory, visual stimulation, visual system parameters, Young Adult
@article{royerEfficientVisualInformation2016,
title = {Efficient visual information for unfamiliar face matching despite viewpoint variations: It's not in the eyes!},
author = {J. Royer and C. Blais and V. Barnabé-Lortie and M. Carré and J. Leclerc and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968779426&doi=10.1016%2fj.visres.2016.04.004&partnerID=40&md5=4c63f6eea279f7322c9af23ae9ed22c1},
doi = {10.1016/j.visres.2016.04.004},
issn = {00426989 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {Vision Research},
volume = {123},
pages = {33–40},
publisher = {Elsevier Ltd},
abstract = {Faces are encountered in highly diverse angles in real-world settings. Despite this considerable diversity, most individuals are able to easily recognize familiar faces. The vast majority of studies in the field of face recognition have nonetheless focused almost exclusively on frontal views of faces. Indeed, a number of authors have investigated the diagnostic facial features for the recognition of frontal views of faces previously encoded in this same view. However, the nature of the information useful for identity matching when the encoded face and test face differ in viewing angle remains mostly unexplored. The present study addresses this issue using individual differences and bubbles, a method that pinpoints the facial features effectively used in a visual categorization task. Our results indicate that the use of features located in the center of the face, the lower left portion of the nose area and the center of the mouth, are significantly associated with individual efficiency to generalize a face's identity across different viewpoints. However, as faces become more familiar, the reliance on this area decreases, while the diagnosticity of the eye region increases. This suggests that a certain distinction can be made between the visual mechanisms subtending viewpoint invariance and face recognition in the case of unfamiliar face identification. Our results further support the idea that the eye area may only come into play when the face stimulus is particularly familiar to the observer. © 2016 Elsevier Ltd.},
keywords = {accuracy, adult, article, association, attention, Bubbles, Evoked Potentials, eye fixation, Face, face profile, face recognition, Facial Recognition, facies, female, Fixation, human, human experiment, Humans, Image analysis, Individual differences, male, Ocular, Pattern Recognition, Photic Stimulation, photostimulation, physiology, priority journal, procedures, Psychophysics, recognition, Recognition (Psychology), regression analysis, task performance, unfamiliar face matching, viewpoint variation, Viewpoint variations, Visual, visual discrimination, visual evoked potential, visual information, visual memory, visual stimulation, visual system parameters, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Faces are encountered in highly diverse angles in real-world settings. Despite this considerable diversity, most individuals are able to easily recognize familiar faces. The vast majority of studies in the field of face recognition have nonetheless focused almost exclusively on frontal views of faces. Indeed, a number of authors have investigated the diagnostic facial features for the recognition of frontal views of faces previously encoded in this same view. However, the nature of the information useful for identity matching when the encoded face and test face differ in viewing angle remains mostly unexplored. The present study addresses this issue using individual differences and bubbles, a method that pinpoints the facial features effectively used in a visual categorization task. Our results indicate that the use of features located in the center of the face, the lower left portion of the nose area and the center of the mouth, are significantly associated with individual efficiency to generalize a face's identity across different viewpoints. However, as faces become more familiar, the reliance on this area decreases, while the diagnosticity of the eye region increases. This suggests that a certain distinction can be made between the visual mechanisms subtending viewpoint invariance and face recognition in the case of unfamiliar face identification. Our results further support the idea that the eye area may only come into play when the face stimulus is particularly familiar to the observer. © 2016 Elsevier Ltd.
10.
Allili, M. S.; Baaziz, N.; Mejri, M.
Texture modeling using contourlets and finite mixtures of generalized gaussian distributions and applications Journal Article
In: IEEE Transactions on Multimedia, vol. 16, no. 3, pp. 772–784, 2014, ISSN: 15209210, (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Contourlet coefficients, Contourlet transform, Defects, Directional information, Fabric texture, face recognition, Generalized Gaussian Distributions, Inspection, Mixtures, Probability density function, Probability density functions (PDFs), State-of-the-art methods, Texture retrieval, Textures
@article{allili_texture_2014,
title = {Texture modeling using contourlets and finite mixtures of generalized gaussian distributions and applications},
author = {M. S. Allili and N. Baaziz and M. Mejri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896903467&doi=10.1109%2fTMM.2014.2298832&partnerID=40&md5=16b2fa741e71e1e581f6b0f54c43a676},
doi = {10.1109/TMM.2014.2298832},
issn = {15209210},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Multimedia},
volume = {16},
number = {3},
pages = {772–784},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions (MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT (SCT) and redundant CT (RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods. © 2014 IEEE.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Contourlet coefficients, Contourlet transform, Defects, Directional information, Fabric texture, face recognition, Generalized Gaussian Distributions, Inspection, Mixtures, Probability density function, Probability density functions (PDFs), State-of-the-art methods, Texture retrieval, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions (MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT (SCT) and redundant CT (RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods. © 2014 IEEE.