(M/F) PhD : Identification and anticipation of facial expressions: the effect of spatial frequency filtering

Job Description

Thesis Subject

PhD subject: Identification and anticipation of facial expressions: the effect of spatial frequency filtering

Project theme: Videoconferencing is more and more frequently used as a collaborative tool. In this context, various types of information are available: the verbal and nonverbal information conveyed by participants, as well as visual content (slideshows, collaborative word-processing documents, shared note-taking documents). For an effective interaction, it is important to be able to rapidly recognize the emotional signals expressed by the partakers' facial expressions. This rapid detection is thought to rely on individuals' ability to anticipate (i.e., automatically extrapolate) the continuation of facial expressions (Prigent et al., 2018). Such anticipation, supported by the predictive brain, may also help compensate for abrupt interruptions of motion, for example when one speaker is suddenly replaced on screen by another person interrupting them.
This rapid anticipation mechanism may rely on the way the brain processes visual information. Neuroscience research has shown that low spatial frequencies and high spatial frequencies in facial expression images are processed differently. Low spatial frequencies are processed rapidly and provide a general understanding of the emotional expression (Vlamings et al., 2009; Wang et al., 2021), whereas higher frequencies take longer to analyze but provide more detailed information about facial expressions. Many studies of this kind have focused on static facial expressions, but few have investigated dynamic expressions, despite their much greater relevance in everyday human interactions. Even fewer studies have examined the predictive mechanisms involved in their perception.
The present project investigates the impact of spatial frequency filtering (high and low spatial frequencies) on the identification of facial expressions of emotions and on the anticipation of the continuation of those facial expressions.
Regarding application perspective, the objective of this doctoral project is to determine how relevant facial information should be displayed (through spatial frequency filtering) in order to assist users in videoconferencing contexts.

References:
Prigent, E., Amorim, M., & De Oliveira, A. M. (2018). Representational momentum in dynamic facial expressions is modulated by the level of expressed pain: Amplitude and direction effects. Attention Perception & Psychophysics, 80(1), 82 93. https://doi.org/10.3758/s13414-017-1422-6
Vlamings, P. H. J. M., Goffaux, V., & Kemner, C. (2009). Is the early modulation of brain activity by fearful facial expressions primarily mediated by coarse low spatial frequency information? Journal of Vision, 9(5), 12-12. https://doi.org/10.1167/9.5.12
Wang, S., Eccleston, C., & Keogh, E. (2021). The Time Course of Facial Expression Recognition Using Spatial Frequency Information : Comparing Pain and Core Emotions. The Journal of Pain, 22(2), 196 208. https://doi.org/10.1016/j.jpain.2020.07.004

Your Work Environment

The candidate will be supervised by Ouriel Grynszpan (Full Professor of Computer Science, AMI Group) and Elise Prigent (Assistant Professor of Psychology, CPU Group). The PhD candidate will join the AMI group (Architectures and Models for Interaction) and collaborate with the CPU group (Cognition Perception and Uses) of the LISN (Laboratoire Interdisciplinaire des Sciences du Numérique). The thesis is part of a project called PRECOG (PREdiction for shared COGnition in collaboration with human or artificial agents) supported by the ANR (French National Research Agency) that includes 5 different laboratories: LISN, PICS-L (Perception, Interactions, Comportements et Simulation des usagers de la route et de la rue, Univ. Gustave Eiffel), LaPEA (laboratoire de Psychologie et d'Ergonomie Appliquées, Univ. Gustave Eiffel), DTIS (Département du Traitement de l'Information et Systèmes, ONERA), and the Jean-Nicod Institut (Ecole Normale Supérieure).

Constraints and risks

None

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

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