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Facial Expression Recognition with Geometric Scattering on 3D Point Clouds.

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This study introduces a novel framework for 3D Facial Expression Recognition (3D FER) using point cloud data. It achieves high accuracy by extracting geometric features robust to noise and variations.

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Area of Science:

  • Computer Vision
  • Geometric Deep Learning
  • 3D Data Analysis

Background:

  • Point clouds are crucial for detailed geometry but susceptible to noise in raw sensor data.
  • Existing 3D Facial Expression Recognition (3D FER) methods struggle with high-dimensional data and environmental variations.
  • Grid-based methods entangle features with pose and illumination variations.

Purpose of the Study:

  • To develop an efficient and robust framework for 3D Facial Expression Recognition (3D FER) directly from point cloud data.
  • To address challenges in feature abstraction and stabilization for noisy 3D facial data.
  • To enable accurate expression recognition without requiring predefined meshes or additional signals.

Main Methods:

  • Proposed a localized and smoothed overlapping kernel for extracting discriminative geometric features from point clouds.
  • Utilized manifold scattering transform to associate deformation stability with external perturbations.
  • Developed a novel framework that directly processes point cloud coordinates for 3D FER.

Main Results:

  • Achieved 78.33% accuracy on the Bosphorus dataset for expression recognition.
  • Attained 77.55% accuracy on the 3D-BUFE dataset.
  • Demonstrated a compact framework capable of direct point cloud consumption for FER.

Conclusions:

  • The proposed framework offers a robust and efficient solution for 3D Facial Expression Recognition (3D FER) using raw point cloud data.
  • The method effectively extracts inherent geometric features, providing stability against extrinsic variations.
  • This approach advances the field by enabling direct point cloud analysis for complex facial expression tasks.