Gaussian mixture 3D morphable face model

作者：

Highlights：

• A Gaussian Mixture 3DMM (GM-3DMM) which models the global population as a mixture of Gaussian subpopulations.

• A ESO-based model selection strategy for GM-3DMM fitting.

• A GM-3DMM-based face recognition framework by fusing multiple experts, which has achieved state-of-the-art result on the Multi-PIE face dataset.

• A new 3D face dataset, SURREY-JNU, comprising 942 3D face scans of people with mixed backgrounds.

摘要

•A Gaussian Mixture 3DMM (GM-3DMM) which models the global population as a mixture of Gaussian subpopulations.•A ESO-based model selection strategy for GM-3DMM fitting.•A GM-3DMM-based face recognition framework by fusing multiple experts, which has achieved state-of-the-art result on the Multi-PIE face dataset.•A new 3D face dataset, SURREY-JNU, comprising 942 3D face scans of people with mixed backgrounds.

论文关键词：Gaussian-mixture model,3D morphable model,3D face reconstruction,Face model fitting,Face recognition

论文评审过程：Received 30 January 2017, Revised 10 July 2017, Accepted 5 September 2017, Available online 9 September 2017, Version of Record 28 October 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.09.006