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.
Chartier, S.; Renaud, P.
Eye-tracker data filtering using pulse coupled neural network Proceedings Article
In: Proceedings of the IASTED International Conference on Modelling and Simulation, pp. 91–96, Montreal, QC, 2006, ISBN: 0-88986-594-9 978-0-88986-594-5, (ISSN: 10218181).
Abstract | Links | BibTeX | Tags: Data reduction, Eye trackers, Eye-tracker, Filter, Median, Neural networks, Noise, Nonlinear filtering, Pulse couple neural network, Pulse coupled neural network (PCNN), Signal to noise ratio, Spurious signal noise, Wave filters
@inproceedings{chartier_eye-tracker_2006,
title = {Eye-tracker data filtering using pulse coupled neural network},
author = {S. Chartier and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33751247311&partnerID=40&md5=ee3f159e47fa32d2710c97826dccdf52},
isbn = {0-88986-594-9 978-0-88986-594-5},
year = {2006},
date = {2006-01-01},
booktitle = {Proceedings of the IASTED International Conference on Modelling and Simulation},
volume = {2006},
pages = {91–96},
address = {Montreal, QC},
abstract = {Data obtained from eye-tracker are contaminated with noise due to eye blink and hardware failure to detect corneal reflection. One solution is to use a nonlinear filter such as the median. However, median filters modify both noisy and noise free data and they are therefore difficult to use in real time applications. To overcome these limits, a simplified pulse coupled neural network (PCNN) is proposed to correctly detect and remove noisy data while leaving uncorrupted data untouched. Results indicated that a filter based on a PCNN achieved a much better performance than the median filter in peak signal-to-noise ratio (PSNR) and in visual inspection.},
note = {ISSN: 10218181},
keywords = {Data reduction, Eye trackers, Eye-tracker, Filter, Median, Neural networks, Noise, Nonlinear filtering, Pulse couple neural network, Pulse coupled neural network (PCNN), Signal to noise ratio, Spurious signal noise, Wave filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Data obtained from eye-tracker are contaminated with noise due to eye blink and hardware failure to detect corneal reflection. One solution is to use a nonlinear filter such as the median. However, median filters modify both noisy and noise free data and they are therefore difficult to use in real time applications. To overcome these limits, a simplified pulse coupled neural network (PCNN) is proposed to correctly detect and remove noisy data while leaving uncorrupted data untouched. Results indicated that a filter based on a PCNN achieved a much better performance than the median filter in peak signal-to-noise ratio (PSNR) and in visual inspection.