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3D facial expression recognition using kernel methods on Riemannian manifold

  • Published:2017-09 Issue: Volume:64 Page:25-32
  • ISSN:0952-1976
  • Container-title:Engineering Applications of Artificial Intelligence
  • language:en
  • Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Hariri Walid,Tabia Hedi,Farah Nadir,Benouareth Abdallah,Declercq David

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference51 articles.

1. Spatially optimized data-level fusion of texture and shape for face recognition;Al-Osaimi;IEEE Trans. Image Process.,2012

2. Towards a robust affect recognition: automatic facial expression recognition in 3d faces;Azazi;Expert Syst. Appl.,2015

3. A set of selected sift features for 3d facial expression recognition;Berretti,2010

4. A survey of approaches and challenges in 3d and multi-modal 3d+ 2d face recognition;Bowyer;Computer Vision and Image Understanding,2006

5. Volterra kernel based face recognition using artificial bee colonyoptimization;Chakrabarty;Eng. Appl. Artif. Intell.,2013

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2. Join multiple Riemannian manifold representation and multi‐kernel non‐redundancy for image clustering;CAAI Transactions on Intelligence Technology;2024-05-08

3. Contrastive Learning of View-invariant Representations for Facial Expressions Recognition;ACM Transactions on Multimedia Computing, Communications, and Applications;2023-12-11

4. Occlusion-aware facial expression recognition: A deep learning approach;Multimedia Tools and Applications;2023-09-25

5. Infrared Thermal Imaging Face Expression Recognition Based on Harris Algorithm;International Journal of Pattern Recognition and Artificial Intelligence;2023-09-25
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