In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration model-building demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model.

本文提出了一种基于高斯过程的新型开源面部配准管道以及其对面部图像分析的应用。通过引入面部对称性、多尺度和空间变化细节，该配准策略和建模技术可以应用于中性面部和面部表情，帮助构建三维可塑面部模型。同时，还发布了BFM-2017的新版本，其中包括更好的年龄分布和额外的面部表情模型，其开源软件框架及其实现的AU分析与综合模型适应（Analysis-by-Synthesis model adaption）已在多PIE和LFW数据库上进行了测试。

可塑面部模型 - 一个开放框架