معرفی
Renaud Seguier is a Professor in the Department of Electronics and Telecommunications at the Institute of Electronics and Telecommunications of Rennes, School of Engineering. His research spans computer vision, signal processing, and human-computer interaction with a focus on facial analysis and speech processing.
His primary research interests include computer vision (specializing in micro-expression recognition, 3D facial modeling, and gaze estimation), signal processing (speech emotion recognition and source-filter decomposition), and affective computing (emotion detection from multimodal inputs). Key methodologies involve deep learning architectures like variational autoencoders, GANs, and specialized metric learning techniques.
His recent publications demonstrate strong trends in multimodal emotion analysis combining audiovisual inputs, unsupervised learning for human activity recognition, and generative model applications for facial editing and deepfake detection. The work shows increasing sophistication in temporal modeling of facial dynamics and disentangled feature representations.
As an academic advisor, he supervises numerous students including Jingting Li, Samir Sadok, and Mouath Aouayeb who frequently appear as first authors on publications. His collaborative network includes Catherine Soladie, Simon Leglaive, and Amine Kacete across multiple institutions.
His laboratory work centers on the 3D facial analysis pipeline involving texture reconstruction, motion denoising, and micro-expression spotting systems. Current projects integrate RGB-D sensing with deep learning for unconstrained environments, particularly focusing on health diagnostics from facial cues and real-time gaze estimation.
