mlsanityData to 7 Oct 2026

Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition

arXiv 2104.09145 ↗First posted 19 Apr 2021cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Face identification/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two disadvantages. First, important facial shape cues are ignored. Second, facial deformations due to expressions can have an impact on the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face identification/recognition based on facial keypoints. Each dynamic sequence of facial expressions is represented as a spatio-temporal graph, which is constructed using 3D facial landmarks. Each graph node contains local shape and texture features that are extracted from its neighborhood. For the classification/identification of faces, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

15 of 39 references.

References · 15

  1. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  2. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
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  11. 2014Web-Scale Training for Face Identificationcited by 20
  12. 2014Dense 3D Face Correspondencecited by 10
3 more
  1. 2017Deep 3D Face Identificationcited by 8
  2. 2019Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognitioncited by 8
  3. 2017A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognitioncited by 6

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Cited by year

Citing papers per year: 2021: 2.

2 papers.

  1. 2021A cappella: Audio-visual Singing Voice Separationcited by 12
  2. 2021Deep and Shallow Covariance Feature Quantization for 3D Facial Expression Recognitioncited by 0