Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Ouyed, O.; Allili, M. S.
Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition Journal Article
In: Expert Systems with Applications, vol. 148, 2020, ISSN: 09574174, (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: Arts computing, Computationally efficient, Gradient descent, Gradient methods, Group sparsities, Group-of-features relevance, Human interaction recognition, Multinomial kernels, regression analysis, Relevance weights, State-of-art methods
@article{ouyed_group–features_2020,
title = {Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition},
author = {O. Ouyed and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078662750&doi=10.1016%2fj.eswa.2020.113247&partnerID=40&md5=8738cfe1a8a6ded1d5f247b8c62de724},
doi = {10.1016/j.eswa.2020.113247},
issn = {09574174},
year = {2020},
date = {2020-01-01},
journal = {Expert Systems with Applications},
volume = {148},
abstract = {We propose an approach for human interaction recognition (HIR) in videos using multinomial kernel logistic regression with group-of-features relevance (GFR-MKLR). Our approach couples kernel and group sparsity modelling to ensure highly precise interaction classification. The group structure in GFR-MKLR is chosen to reflect a representation of interactions at the level of gestures, which ensures more robustness to intra-class variability due to occlusions and changes in subject appearance, body size and viewpoint. The groups consist of motion features extracted from tracking interacting persons joints over time. We encode group sparsity in GFR-MKLR through relevance weights reflecting each group (gesture) discrimination capability between different interaction categories. These weights are automatically estimated during GFR-MKLR training using gradient descent minimisation. Our model is computationally efficient and can be trained on a small training dataset while maintaining a good generalization and interpretation capabilities. Experiments on the well-known UT-Interaction dataset have demonstrated the performance of our approach by comparison with state-of-art methods. © 2020 Elsevier Ltd},
note = {Publisher: Elsevier Ltd},
keywords = {Arts computing, Computationally efficient, Gradient descent, Gradient methods, Group sparsities, Group-of-features relevance, Human interaction recognition, Multinomial kernels, regression analysis, Relevance weights, State-of-art methods},
pubstate = {published},
tppubtype = {article}
}
We propose an approach for human interaction recognition (HIR) in videos using multinomial kernel logistic regression with group-of-features relevance (GFR-MKLR). Our approach couples kernel and group sparsity modelling to ensure highly precise interaction classification. The group structure in GFR-MKLR is chosen to reflect a representation of interactions at the level of gestures, which ensures more robustness to intra-class variability due to occlusions and changes in subject appearance, body size and viewpoint. The groups consist of motion features extracted from tracking interacting persons joints over time. We encode group sparsity in GFR-MKLR through relevance weights reflecting each group (gesture) discrimination capability between different interaction categories. These weights are automatically estimated during GFR-MKLR training using gradient descent minimisation. Our model is computationally efficient and can be trained on a small training dataset while maintaining a good generalization and interpretation capabilities. Experiments on the well-known UT-Interaction dataset have demonstrated the performance of our approach by comparison with state-of-art methods. © 2020 Elsevier Ltd
2.
Ouyed, O.; Allili, M. S.
Recognizing human interactions using group feature relevance in multinomial kernel logistic regression Proceedings Article
In: K., Rus V. Brawner (Ed.): Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018, pp. 541–545, AAAI press, 2018, ISBN: 978-1-57735-796-4.
Abstract | Links | BibTeX | Tags: Art methods, Artificial intelligence, Feature relevance, Group sparsities, Human interactions, Image features, Kernel logistic regression, Multinomial kernels, regression analysis, Sparse models
@inproceedings{ouyed_recognizing_2018,
title = {Recognizing human interactions using group feature relevance in multinomial kernel logistic regression},
author = {O. Ouyed and M. S. Allili},
editor = {Rus V. Brawner K.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067977954&partnerID=40&md5=1c8d720de570fc565bca3741c107bc9a},
isbn = {978-1-57735-796-4},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018},
pages = {541–545},
publisher = {AAAI press},
abstract = {We propose a supervised approach incorporating group feature sparsity in multi-class kernel logistic regression (GFR-MKLR). The need for group sparsity arises in several practical situations where a subset of a set of factors can explain a predicted variable and each factor consists of a group of variables. We apply our approach for predicting human interactions based on body parts motion (e.g., hands, legs, head, etc.) where image features are organised in groups corresponding to body parts. Our approach, leads to sparse models by assigning weights to groups of features having the highest discrimination between different types of interactions. Experiments conducted on the UT-Interaction dataset have demonstrated the performance of our method with regard to stat-of-art methods. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.},
keywords = {Art methods, Artificial intelligence, Feature relevance, Group sparsities, Human interactions, Image features, Kernel logistic regression, Multinomial kernels, regression analysis, Sparse models},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a supervised approach incorporating group feature sparsity in multi-class kernel logistic regression (GFR-MKLR). The need for group sparsity arises in several practical situations where a subset of a set of factors can explain a predicted variable and each factor consists of a group of variables. We apply our approach for predicting human interactions based on body parts motion (e.g., hands, legs, head, etc.) where image features are organised in groups corresponding to body parts. Our approach, leads to sparse models by assigning weights to groups of features having the highest discrimination between different types of interactions. Experiments conducted on the UT-Interaction dataset have demonstrated the performance of our method with regard to stat-of-art methods. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
3.
Ouyed, O.; Allili, M. S.
Feature weighting for multinomial kernel logistic regression and application to action recognition Journal Article
In: Neurocomputing, vol. 275, pp. 1752–1768, 2018, ISSN: 09252312, (Publisher: Elsevier B.V.).
Abstract | Links | BibTeX | Tags: Action recognition, article, Classification, classification algorithm, Classification performance, Computer applications, controlled study, embedding, Feature relevance, feature relevance for multinomial kernel logistic regression, Feature weighting, Kernel logistic regression, kernel method, Learning, mathematical computing, Multinomial kernels, multinominal kernel logistic regression, Neural networks, priority journal, recognition, regression analysis, simulation, sparse modeling, Sparse models, sparse multinomial logistic regression, sparsity promoting regularization, standard, Supervised classification
@article{ouyed_feature_2018,
title = {Feature weighting for multinomial kernel logistic regression and application to action recognition},
author = {O. Ouyed and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035104467&doi=10.1016%2fj.neucom.2017.10.024&partnerID=40&md5=09687b392a405be4338799a750932cf3},
doi = {10.1016/j.neucom.2017.10.024},
issn = {09252312},
year = {2018},
date = {2018-01-01},
journal = {Neurocomputing},
volume = {275},
pages = {1752–1768},
abstract = {Multinominal kernel logistic regression (MKLR) is a supervised classification method designed for separating classes with non-linear boundaries. However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data. We propose an approach for embedding feature relevance in multinomial kernel logistic regression. Our approach, coined fr-MKLR, generalizes MKLR by introducing a feature weighting scheme in the Gaussian kernel and using the so-called ℓ0-“norm” as sparsity-promoting regularization. Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification. Application of our approach to several standard datasets and video action recognition has provided very promising results compared to other methods. © 2017 Elsevier B.V.},
note = {Publisher: Elsevier B.V.},
keywords = {Action recognition, article, Classification, classification algorithm, Classification performance, Computer applications, controlled study, embedding, Feature relevance, feature relevance for multinomial kernel logistic regression, Feature weighting, Kernel logistic regression, kernel method, Learning, mathematical computing, Multinomial kernels, multinominal kernel logistic regression, Neural networks, priority journal, recognition, regression analysis, simulation, sparse modeling, Sparse models, sparse multinomial logistic regression, sparsity promoting regularization, standard, Supervised classification},
pubstate = {published},
tppubtype = {article}
}
Multinominal kernel logistic regression (MKLR) is a supervised classification method designed for separating classes with non-linear boundaries. However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data. We propose an approach for embedding feature relevance in multinomial kernel logistic regression. Our approach, coined fr-MKLR, generalizes MKLR by introducing a feature weighting scheme in the Gaussian kernel and using the so-called ℓ0-“norm” as sparsity-promoting regularization. Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification. Application of our approach to several standard datasets and video action recognition has provided very promising results compared to other methods. © 2017 Elsevier B.V.
4.
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},
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.
5.
Côté, S.; Bouchard, S.
Cognitive mechanisms underlying virtual reality exposure Journal Article
In: Cyberpsychology and Behavior, vol. 12, no. 2, pp. 121–129, 2009, ISSN: 10949313 (ISSN).
Abstract | Links | BibTeX | Tags: Adaptation, adult, aged, Animals, arachnophobia, Arousal, article, avoidance behavior, cardiovascular response, clinical article, cognition, Culture, female, Heart Rate, human, Humans, Implosive Therapy, male, Middle Aged, Personality Assessment, phobia, Phobic Disorders, prediction, Psychological, questionnaire, Questionnaires, regression analysis, scoring system, Self Efficacy, spider, Spiders, structured interview, task performance, treatment outcome, User-Computer Interface, virtual reality, Young Adult
@article{cote_cognitive_2009,
title = {Cognitive mechanisms underlying virtual reality exposure},
author = {S. Côté and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-64749106909&doi=10.1089%2fcpb.2008.0008&partnerID=40&md5=e9bd263ea9e1940b66910d9651bd119e},
doi = {10.1089/cpb.2008.0008},
issn = {10949313 (ISSN)},
year = {2009},
date = {2009-01-01},
journal = {Cyberpsychology and Behavior},
volume = {12},
number = {2},
pages = {121–129},
abstract = {Many studies have assessed virtual reality exposures efficacy, but very few examined its treatment processes. The addition of objective measures of arousal and information processing mechanisms would be a valuable contribution in order to provide a more complete and detailed picture. The goal of this study was to better document the cognitive mechanisms associated with therapeutic change after an in virtuo exposure treatment. Twenty-eight adults suffering from arachnophobia were assessed and received an exposure-based treatment using virtual reality. General outcome and specific processes measures included a battery of standardized questionnaires, a pictorial emotional Stroop task, a Behavioral Avoidance Test, and a measure of participants' cardiac response while they looked at a live tarantula. The analyses showed that changes in perceived self-efficacy and dysfunctional beliefs were the best predictors of change in general outcome and cardiac response; change in dysfunctional beliefs were the best predictor of change in behavioral avoidance. These innovative results provide a very detailed and organized picture of the complex cognitive mechanisms involved in therapeutic change following in virtuo exposure for arachnophobia. © 2009 Mary Ann Liebert, Inc.},
keywords = {Adaptation, adult, aged, Animals, arachnophobia, Arousal, article, avoidance behavior, cardiovascular response, clinical article, cognition, Culture, female, Heart Rate, human, Humans, Implosive Therapy, male, Middle Aged, Personality Assessment, phobia, Phobic Disorders, prediction, Psychological, questionnaire, Questionnaires, regression analysis, scoring system, Self Efficacy, spider, Spiders, structured interview, task performance, treatment outcome, User-Computer Interface, virtual reality, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Many studies have assessed virtual reality exposures efficacy, but very few examined its treatment processes. The addition of objective measures of arousal and information processing mechanisms would be a valuable contribution in order to provide a more complete and detailed picture. The goal of this study was to better document the cognitive mechanisms associated with therapeutic change after an in virtuo exposure treatment. Twenty-eight adults suffering from arachnophobia were assessed and received an exposure-based treatment using virtual reality. General outcome and specific processes measures included a battery of standardized questionnaires, a pictorial emotional Stroop task, a Behavioral Avoidance Test, and a measure of participants' cardiac response while they looked at a live tarantula. The analyses showed that changes in perceived self-efficacy and dysfunctional beliefs were the best predictors of change in general outcome and cardiac response; change in dysfunctional beliefs were the best predictor of change in behavioral avoidance. These innovative results provide a very detailed and organized picture of the complex cognitive mechanisms involved in therapeutic change following in virtuo exposure for arachnophobia. © 2009 Mary Ann Liebert, Inc.
6.
Robillard, G.; Bouchard, S.; Fournier, T.; Renaud, P.
In: Cyberpsychology and Behavior, vol. 6, no. 5, pp. 467–476, 2003, ISSN: 10949313 (ISSN).
Abstract | Links | BibTeX | Tags: Adolescent, adult, Anxiety, article, clinical article, computer, computer program, Computer Simulation, Computer-Assisted, controlled study, correlation analysis, Desensitization, emotion, exposure, female, game, human, Humans, male, Matched-Pair Analysis, Middle Aged, Neuropsychological Tests, phobia, Phobic Disorders, Psychologic, psychotherapy, Reality Testing, Reference Values, regression analysis, Self Concept, Space Perception, symptom, Therapy, User-Computer Interface, Video Games, virtual reality, visual stimulation
@article{robillard_anxiety_2003,
title = {Anxiety and Presence during VR Immersion: A Comparative Study of the Reactions of Phobic and Non-phobic Participants in Therapeutic Virtual Environments Derived from Computer Games},
author = {G. Robillard and S. Bouchard and T. Fournier and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0142063106&doi=10.1089%2f109493103769710497&partnerID=40&md5=0d245828ebefb17548822c4c316f5721},
doi = {10.1089/109493103769710497},
issn = {10949313 (ISSN)},
year = {2003},
date = {2003-01-01},
journal = {Cyberpsychology and Behavior},
volume = {6},
number = {5},
pages = {467–476},
abstract = {Virtual reality can be used to provide phobic clients with therapeutic exposure to phobogenic stimuli. However, purpose-built therapeutic VR hardware and software can be expensive and difficult to adapt to individual client needs. In this study, inexpensive and readily adaptable PC computer games were used to provide exposure therapy to 13 phobic participants and 13 non-phobic control participants. It was found that anxiety could be induced in phobic participants by exposing them to phobogenic stimuli in therapeutic virtual environments derived from computer games (TVEDG). Assessments were made of the impact of simulator sickness and of sense of presence on the phobogenic effectiveness of TVEDGs. Participants reported low levels of simulator sickness, and the results indicate that simulator sickness had no significant impact on either anxiety or sense of presence. Group differences, correlations, and regression analyses indicate a synergistic relationship between presence and anxiety. These results do not support Slater's contention that presence and emotion are orthogonal.},
keywords = {Adolescent, adult, Anxiety, article, clinical article, computer, computer program, Computer Simulation, Computer-Assisted, controlled study, correlation analysis, Desensitization, emotion, exposure, female, game, human, Humans, male, Matched-Pair Analysis, Middle Aged, Neuropsychological Tests, phobia, Phobic Disorders, Psychologic, psychotherapy, Reality Testing, Reference Values, regression analysis, Self Concept, Space Perception, symptom, Therapy, User-Computer Interface, Video Games, virtual reality, visual stimulation},
pubstate = {published},
tppubtype = {article}
}
Virtual reality can be used to provide phobic clients with therapeutic exposure to phobogenic stimuli. However, purpose-built therapeutic VR hardware and software can be expensive and difficult to adapt to individual client needs. In this study, inexpensive and readily adaptable PC computer games were used to provide exposure therapy to 13 phobic participants and 13 non-phobic control participants. It was found that anxiety could be induced in phobic participants by exposing them to phobogenic stimuli in therapeutic virtual environments derived from computer games (TVEDG). Assessments were made of the impact of simulator sickness and of sense of presence on the phobogenic effectiveness of TVEDGs. Participants reported low levels of simulator sickness, and the results indicate that simulator sickness had no significant impact on either anxiety or sense of presence. Group differences, correlations, and regression analyses indicate a synergistic relationship between presence and anxiety. These results do not support Slater's contention that presence and emotion are orthogonal.