Morphable Face Models - An Open Framework (1709.08398v2)

Abstract: 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 and 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.

• The paper introduces an open-source pipeline that decouples face registration from algorithmic constraints using Gaussian process morphable models.
• It employs multi-scale and mirror symmetric kernel techniques to capture detailed geometric variability and dynamic facial expressions.
• The framework, validated on BU3D-FE, Multi-PIE, and LFW databases, enhances 3D morphable face modeling and promotes reproducible research.

Analysis of "Morphable Face Models - An Open Framework"

The paper "Morphable Face Models - An Open Framework" by Gerig et al. presents a significant advancement in the domain of face registration through the development of a novel open-source pipeline. This pipeline focuses on non-rigid registration of faces, a crucial element in constructing 3D Morphable Face Models (3DMMs), implemented through the use of Gaussian Process Morphable Models (GPMMs). Unlike conventional methods, the authors introduce a sophisticated mechanism to decouple problem-specific requirements from the registration algorithm using domain-specific priors modeled as Gaussian processes.

The paper delineates several key contributions. First, it outlines a strategy for face registration capturing symmetries, multi-scale, and spatially-varying details, which are essential for handling the neutral faces and diverse facial expressions. Furthermore, the authors extend their methodology to create an innovative open-source software framework capable of executing face registration and model-building. This software has been validated on the BU3D-FE database and is adaptable to 2D face images via an Analysis-by-Synthesis model tested on the Multi-PIE and LFW databases.

A significant addition to the field is the release of an updated Basel Face Model (BFM-2017). This new model improves upon previous iterations by refining the age distribution and incorporating an additional facial expression model, thus broadening its applicability to a wider range of demographic and expressive characteristics.

From a methodological standpoint, the paper introduces Gaussian process-based kernel modeling techniques for face registration. The authors demonstrate a structured approach to incrementally build a deformation prior, which includes:

1. Multi-scale B-spline kernels to address geometric variability.
2. A mirror symmetric kernel to encapsulate the inherent symmetry of facial features.
3. A statistical shape model kernel crafted to capture dynamic facial expressions, primarily focusing on mouth movements.

The registration algorithm is robust, employing Gaussian process regression to integrate landmark data effectively and iterating with decreasing regularization weights for optimal results. This algorithm is assessed through landmark accuracy on the BU-3DFE dataset, showing performance metrics commensurate with state-of-the-art methods in the field.

In the context of applications, the paper showcases the utility of the developed model for inverse rendering in 2D face image scenarios, lending itself to potent applications in face recognition. Notably, the qualitative results on the Multi-PIE and LFW databases affirm the model's adaptability and robustness across varying poses and expressions, further supporting its potential for wide adoption.

While the explicit numerical results and comparative analyses provide validation of the model's efficacy, the broader implications suggest significant avenues for future development. The seamless integration of domain-specific knowledge and algorithmic flexibility offers a versatile tool for further innovation in 3D facial modeling, potentially impacting facial recognition, animation, and personalized avatar creation.

The open-source release of both the software pipeline and the new BFM-2017 model represents a strategic effort to facilitate reproducibility and foster collaboration within the research community, enabling further refinement and benchmarking of techniques in non-rigid face registration.

In conclusion, this paper contributes a well-structured, reproducible framework for 3DMM construction and offers a rich resource for advancing facial analysis research. The methodologies and tools presented are likely to shape ongoing and future endeavors in both academic and applied settings within computer vision and beyond.