PointFace: Point Cloud Encoder-Based Feature Embedding for 3-D Face Recognition

Changyuan Jiang, Shisong Lin, Wei Chen, Feng Liu, Linlin Shen

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)


The accuracy of 2D face recognition (FR) has progressed significantly due to the availability of large-scale training data. However, the research of deep learning based 3D FR is still in the early stage. Most of available 3D FR generate 2D maps from 3D data and apply existing 2D CNNs to the generated 2D maps for feature extraction. We propose in this paper a light-weight framework, named PointFace, to directly process point set data for 3D FR. In this framework, two weight-shared encoders are designed to directly extract features from a pair of 3D faces and the distances between embeddings of the same person and different person are minimized and maximized, respectively. The framework also use a feature similarity loss to guide the encoders to obtain discriminative face representations. A pair selection strategy is proposed to generate positive and negative face pairs to further improve the FR performance. Extensive experiments on Lock3DFace and Bosphorus show that the proposed PointFace outperforms state-of-the-art 2D CNN based FR methods.

Original languageEnglish
Pages (from-to)486-497
Number of pages12
JournalIEEE Transactions on Biometrics, Behavior, and Identity Science
Issue number4
Publication statusPublished - 1 Oct 2022
Externally publishedYes


  • 3D face recognition
  • CNN
  • deep learning
  • point cloud processing

ASJC Scopus subject areas

  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence


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