Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Côté, L.; Lamontagne, J.; Bellerose, A.; Blais, C.; Fiset, D.
The eyes are central to face detection: revisiting the foundations of face processing Journal Article
In: Vision Research, vol. 243, 2026, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult
@article{cote_eyes_2026,
title = {The eyes are central to face detection: revisiting the foundations of face processing},
author = {L. Côté and J. Lamontagne and A. Bellerose and C. Blais and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105030389147&doi=10.1016%2Fj.visres.2026.108785&partnerID=40&md5=752aa5d9923ac60539e36118ad41e1e6},
doi = {10.1016/j.visres.2026.108785},
issn = {00426989 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Vision Research},
volume = {243},
abstract = {Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).},
keywords = {adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).
2.
Côté, L.; Lamontagne, J.; Bellerose, A.; Blais, C.; Fiset, D.
The eyes are central to face detection: revisiting the foundations of face processing Journal Article
In: Vision Research, vol. 243, 2026, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Eye, Face, Faces, Facial Recognition, facies, female, human, human experiment, Humans, Image analysis, information processing, Information use, male, Noise, normal human, Pattern Recognition, perception, Photic Stimulation, photostimulation, physiology, procedures, Prosopagnosia, psychology, recognition, Social Perception, spatial frequency discrimination, task performance, Visual, visual discrimination, visual pattern recognition, Young Adult
@article{coteEyesAreCentral2026,
title = {The eyes are central to face detection: revisiting the foundations of face processing},
author = {L. Côté and J. Lamontagne and A. Bellerose and C. Blais and D. Fiset},
url = {https://www.scopus.com/pages/publications/105030389147?origin=resultslist},
doi = {10.1016/j.visres.2026.108785},
issn = {00426989 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Vision Research},
volume = {243},
publisher = {Elsevier Ltd},
abstract = {Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).},
keywords = {adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Eye, Face, Faces, Facial Recognition, facies, female, human, human experiment, Humans, Image analysis, information processing, Information use, male, Noise, normal human, Pattern Recognition, perception, Photic Stimulation, photostimulation, physiology, procedures, Prosopagnosia, psychology, recognition, Social Perception, spatial frequency discrimination, task performance, Visual, visual discrimination, visual pattern recognition, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).
3.
Côté, L.; Lamontagne, J.; Bellerose, A.; Blais, C.; Fiset, D.
The eyes are central to face detection: revisiting the foundations of face processing Journal Article
In: Vision Research, vol. 243, 2026, ISSN: 00426989 (ISSN).
Abstract | Links | BibTeX | Tags: adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult
@article{coteEyesAreCentral2026a,
title = {The eyes are central to face detection: revisiting the foundations of face processing},
author = {L. Côté and J. Lamontagne and A. Bellerose and C. Blais and D. Fiset},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105030389147&doi=10.1016%2Fj.visres.2026.108785&partnerID=40&md5=752aa5d9923ac60539e36118ad41e1e6},
doi = {10.1016/j.visres.2026.108785},
issn = {00426989 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Vision Research},
volume = {243},
abstract = {Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).},
keywords = {adult, article, Black person, Bubbles, Categorization, Caucasian, Detection, emotion assessment, Faces, Facial Recognition, facies, female, human, human experiment, Image analysis, information processing, Information use, male, Noise, normal human, perception, Prosopagnosia, spatial frequency discrimination, task performance, visual discrimination, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Face detection feels effortless, yet it requires finely tuned computations to extract socially meaningful signals from the visual stream. Here, we used the Bubbles method to isolate the facial features and spatial frequency information that support face categorization. Across three experiments varying in task demands and visual context, the eye region consistently emerged as the most diagnostic source of information, particularly in high spatial frequencies. This finding held whether participants distinguished faces from noise, from non-face objects, or from real-world categories—suggesting that the eyes serve as an anchor point for categorization across contexts. Strikingly, this diagnostic profile mirrors that found in face identification tasks, implying that detection and recognition may rely on shared perceptual mechanisms rather than sequential, independent processes. This overlap sheds light on longstanding ambiguities in the prosopagnosia literature, indicating that detection impairments found in patients may stem from a broader failure to extract critical eye information. More broadly, our results invite a rethinking of the early stages of face processing, suggesting that detection already involves selective use of diagnostic facial features that supports recognition, emotional decoding, and social perception. © 2026 The Author(s).
4.
Nouboukpo, A.; Allili, M. S.
Spatially-coherent segmentation using hierarchical gaussian mixture reduction based on cauchy-schwarz divergence Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11662 LNCS, pp. 388–396, 2019, ISSN: 03029743, (ISBN: 9783030272012).
Abstract | Links | BibTeX | Tags: Cauchy-Schwarz divergence, Foreground segmentation, Gaussian distribution, Gaussian Mixture Model, Gaussian mixture reduction, Image analysis, Image segmentation, Mixture reductions, Reduction algorithms, Reduction techniques, State-of-art methods
@article{nouboukpo_spatially-coherent_2019,
title = {Spatially-coherent segmentation using hierarchical gaussian mixture reduction based on cauchy-schwarz divergence},
author = {A. Nouboukpo and M. S. Allili},
editor = {Campilho A. Yu A. Karray F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071452890&doi=10.1007%2f978-3-030-27202-9_35&partnerID=40&md5=2689080f7b2410040a038f080ef93bfa},
doi = {10.1007/978-3-030-27202-9_35},
issn = {03029743},
year = {2019},
date = {2019-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {11662 LNCS},
pages = {388–396},
publisher = {Springer Verlag},
abstract = {Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods. © Springer Nature Switzerland AG 2019.},
note = {ISBN: 9783030272012},
keywords = {Cauchy-Schwarz divergence, Foreground segmentation, Gaussian distribution, Gaussian Mixture Model, Gaussian mixture reduction, Image analysis, Image segmentation, Mixture reductions, Reduction algorithms, Reduction techniques, State-of-art methods},
pubstate = {published},
tppubtype = {article}
}
Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods. © Springer Nature Switzerland AG 2019.
5.
Nouboukpo, A.; Allili, M. S.
Spatially-coherent segmentation using hierarchical gaussian mixture reduction based on cauchy-schwarz divergence Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11662 LNCS, pp. 388–396, 2019, ISSN: 03029743, (ISBN: 9783030272012).
Abstract | Links | BibTeX | Tags: Cauchy-Schwarz divergence, Foreground segmentation, Gaussian distribution, Gaussian Mixture Model, Gaussian mixture reduction, Image analysis, Image segmentation, Mixture reductions, Reduction algorithms, Reduction techniques, State-of-art methods
@article{nouboukpoSpatiallycoherentSegmentationUsing2019,
title = {Spatially-coherent segmentation using hierarchical gaussian mixture reduction based on cauchy-schwarz divergence},
author = {A. Nouboukpo and M. S. Allili},
editor = {Campilho A. Yu A. Karray F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071452890&doi=10.1007%2f978-3-030-27202-9_35&partnerID=40&md5=2689080f7b2410040a038f080ef93bfa},
doi = {10.1007/978-3-030-27202-9_35},
issn = {03029743},
year = {2019},
date = {2019-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {11662 LNCS},
pages = {388–396},
publisher = {Springer Verlag},
abstract = {Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods. © Springer Nature Switzerland AG 2019.},
note = {ISBN: 9783030272012},
keywords = {Cauchy-Schwarz divergence, Foreground segmentation, Gaussian distribution, Gaussian Mixture Model, Gaussian mixture reduction, Image analysis, Image segmentation, Mixture reductions, Reduction algorithms, Reduction techniques, State-of-art methods},
pubstate = {published},
tppubtype = {article}
}
Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods. © Springer Nature Switzerland AG 2019.
6.
Audet, F.; Allili, M. S.; Cretu, A. -M.
Salient object detection in images by combining objectness clues in the RGBD space Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10317 LNCS, pp. 247–255, 2017, ISSN: 03029743, (ISBN: 9783319598758).
Abstract | Links | BibTeX | Tags: Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines
@article{audet_salient_2017,
title = {Salient object detection in images by combining objectness clues in the RGBD space},
author = {F. Audet and M. S. Allili and A. -M. Cretu},
editor = {Campilho A. Karray F. Cheriet F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229105&doi=10.1007%2f978-3-319-59876-5_28&partnerID=40&md5=d78eb69cecd0a34ca2d517cfee44ef54},
doi = {10.1007/978-3-319-59876-5_28},
issn = {03029743},
year = {2017},
date = {2017-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10317 LNCS},
pages = {247–255},
publisher = {Springer Verlag},
abstract = {We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.},
note = {ISBN: 9783319598758},
keywords = {Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.
7.
Audet, F.; Allili, M. S.; Cretu, A. -M.
Salient object detection in images by combining objectness clues in the RGBD space Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10317 LNCS, pp. 247–255, 2017, ISSN: 03029743, (ISBN: 9783319598758).
Abstract | Links | BibTeX | Tags: Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines
@article{audetSalientObjectDetection2017,
title = {Salient object detection in images by combining objectness clues in the RGBD space},
author = {F. Audet and M. S. Allili and A. -M. Cretu},
editor = {Campilho A. Karray F. Cheriet F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022229105&doi=10.1007%2f978-3-319-59876-5_28&partnerID=40&md5=d78eb69cecd0a34ca2d517cfee44ef54},
doi = {10.1007/978-3-319-59876-5_28},
issn = {03029743},
year = {2017},
date = {2017-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10317 LNCS},
pages = {247–255},
publisher = {Springer Verlag},
abstract = {We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.},
note = {ISBN: 9783319598758},
keywords = {Color, Color information, Depth information, Image analysis, Multistage approach, Object detection, Object recognition, Potential region, Real-world image, Salient object detection, Salient objects, Statistical distribution, Voting machines},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-stage approach for salient object detection in natural images which incorporates color and depth information. In the first stage, color and depth channels are explored separately through objectness-based measures to detect potential regions containing salient objects. This procedure produces a list of bounding boxes which are further filtered and refined using statistical distributions. The retained candidates from both color and depth channels are then combined using a voting system. The final stage consists of combining the extracted candidates from color and depth channels using a voting system that produces a final map narrowing the location of the salient object. Experimental results on real-world images have proved the performance of the proposed method in comparison with the case where only color information is used. © Springer International Publishing AG 2017.
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.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-graph based salient object detection Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9730, pp. 318–324, 2016, ISSN: 03029743, (ISBN: 9783319415000).
Abstract | Links | BibTeX | Tags: Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects
@article{filali_multi-graph_2016,
title = {Multi-graph based salient object detection},
author = {I. Filali and M. S. Allili and N. Benblidia},
editor = {Karray F. Campilho A. Campilho A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978804496&doi=10.1007%2f978-3-319-41501-7_36&partnerID=40&md5=eb519756d2e72245e4131d5dc0b416b5},
doi = {10.1007/978-3-319-41501-7_36},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9730},
pages = {318–324},
publisher = {Springer Verlag},
abstract = {We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.},
note = {ISBN: 9783319415000},
keywords = {Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.
10.
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.