Face recognition based on fitting a 3D morphable model

This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.     

Multi-View AAM Fitting and Construction

Active Appearance Models (AAMs) are generative, parametric models that have been successfully used in the past to model deformable objects such as human faces. The original AAMs formulation was 2D, but they have recently been extended to include a 3D shape model. A variety of single-view algorithms exist for fitting and constructing 3D AAMs but one area that has not been studied is multi-view algorithms. In this paper we present multi-view algorithms for both fitting and constructing 3D AAMs. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. In the first part of the paper we describe an algorithm for fitting a single AAM to multiple images, captured simultaneously by cameras with arbitrary locations, rotations, and response functions. This algorithm uses the scaled orthographic imaging model used by previous authors, and in the process of fitting computes, or calibrates, the scaled orthographic camera matrices. In the second part of the paper we describe an extension of this algorithm to calibrate weak perspective (or full perspective) camera models for each of the cameras. In essence, we use the human face as a (non-rigid) calibration grid. We demonstrate that the performance of this algorithm is roughly comparable to a standard algorithm using a calibration grid. In the third part of the paper, we show how camera calibration improves the performance of AAM fitting. A variety of non-rigid structure-from-motion algorithms, both single-view and multi-view, have been proposed that can be used to construct the corresponding 3D non-rigid shape models of a 2D AAM. In the final part of the paper, we show that constructing a 3D face model using non-rigid structure-from-motion suffers from the Bas-Relief ambiguity and may result in a “scaled” (stretched/compressed) model. We outline a robust non-rigid motion-stereo algorithm for calibrated multi-view 3D AAM construction and show how using calibrated multi-view motion-stereo can eliminate the Bas-Relief ambiguity and yield face models with higher 3D fidelity. Electronic Supplementary Material The online version of this article () contains supplementary material, which is available to authorized users.     

基于特征点加细的多分辨率人脸形变模型及人脸建模

提出基于特征点加细的原型三维人脸对应方法建立多分辨人脸形变模型,并根据该形变模型的特点使用多分辨模型匹配方法由单张正面人脸图像进行三维人脸建模。该方法以人脸模型上的眼、眉、口、鼻等主要几何特征为基准点标注基础网格,然后通过加细特征点网格完成原型人脸之间的对应,进而建立多分辨率的形变模型;根据形变模型的构造特点,把待匹配图像按照与模型相同方式进行加细,然后进行多分辨的人脸模型匹配。实验结果表明,新的对应算法可以有效地实现原型三维人脸之间的对应,能够克服传统的光流对应算法对应效果差,算法精度低的缺陷,提高形变模型的精度。新的匹配算法不仅能够加速模型的匹配速度,而且可提高模型匹配的效率和精度,缩短模型匹配的时间。     

基于形变模型的多角度三维人脸实时重建

摘 要：采用人脸特征点调整三维形变模型的方法应用于面部三维重建,但模型形变的计 算往往会产生误差,且耗时较长。因此运用人脸二维特征点对通用三维形变模型的拟合方法进 行改进,提出了一种视频流的多角度实时三维人脸重建方法。首先利用带有三层卷积网络的 CLNF 算法识别二维特征点,并跟踪特征点位置;然后由五官特征点位置估计头部姿态,更新 模型的表情系数,其结果再作用于 PCA 形状系数,促使当前三维模型发生形变;最后采用 ISOMAP 算法提取网格纹理信息,进行纹理融合形成特定人脸模型。实验结果表明,该方法在 人脸重建过程中具有更好的实时性能,且精确度有所提高。     

2D face fitting-assisted 3D face reconstruction for pose-robust face recognition

Recent face recognition algorithm can achieve high accuracy when the tested face samples are frontal. However, when the face pose changes largely, the performance of existing methods drop drastically. Efforts on pose-robust face recognition are highly desirable, especially when each face class has only one frontal training sample. In this study, we propose a 2D face fitting-assisted 3D face reconstruction algorithm that aims at recognizing faces of different poses when each face class has only one frontal training sample. For each frontal training sample, a 3D face is reconstructed by optimizing the parameters of 3D morphable model (3DMM). By rotating the reconstructed 3D face to different views, pose virtual face images are generated to enlarge the training set of face recognition. Different from the conventional 3D face reconstruction methods, the proposed algorithm utilizes automatic 2D face fitting to assist 3D face reconstruction. We automatically locate 88 sparse points of the frontal face by 2D face-fitting algorithm. Such 2D face-fitting algorithm is so-called Random Forest Embedded Active Shape Model, which embeds random forest learning into the framework of Active Shape Model. Results of 2D face fitting are added to the 3D face reconstruction objective function as shape constraints. The optimization objective energy function takes not only image intensity, but also 2D fitting results into account. Shape and texture parameters of 3DMM are thus estimated by fitting the 3DMM to the 2D frontal face sample, which is a non-linear optimization problem. We experiment the proposed method on the publicly available CMUPIE database, which includes faces viewed from 11 different poses, and the results show that the proposed method is effective and the face recognition results toward pose variants are promising.     

基于压缩感知理论的3维人脸快速重建

形变模型是当前人脸重建研究中的一种主要方法。针对形变模型方法中模型构建的缺陷,提出一种基于压缩感知理论的快速三维人脸重建方法。首先,利用压缩感知理论估计三维原型人脸与目标人脸的形状相似性,根据相似性对原型样本进行筛选并构建相应的形变模型,提高建模精度和效率;然后,利用特征点信息进行稀疏模型匹配,并结合径向基函数插值重建生成特定的三维人脸,提高重建表面的平滑性。在BJUT三维数据库和CAS_PEAL二维数据库上的实验结果表明,与经典方法相比,本文方法能够有效地提高重建精度和速度,重建人脸具有较强真实感。     

Generative face alignment through 2.5D active appearance models

This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from single view images. Two model fitting algorithms and their computational efficient approximations are proposed: the Simultaneous Forwards Additive (SFA) and the Normalization Forwards Additive (NFA), both based on the Lucas–Kanade framework. The SFA algorithm searches for shape and appearance parameters simultaneously whereas the NFA projects out the appearance from the error image and searches only for the shape parameters. SFA is therefore more accurate. Robust solutions for the SFA and NFA are also proposed in order to take into account the self-occlusion or partial occlusion of the face. Several performance evaluations for the SFA, NFA and theirs efficient approximations were performed. The experiments include evaluating the frequency of converge, the fitting performance in unseen data and the tracking performance in the FGNET Talking Face sequence. All results show that the 2.5D AAM can outperform both the 2D + 3D combined models and the 2D standard methods. The robust extensions to occlusion were tested on a synthetic sequence showing that the model can deal efficiently with large head rotation.     

基于特征分块的三维人脸重建和识别

针对传统三维人脸重建算法效率低且难以满足实际应用的缺陷,提出一种基于特征分块的三维人脸重建算法,并将此算法应用到三维人脸识别中,实现了基于特征分块的加权三维人脸识别。首先,利用基于平面模板的非均匀重采样法对原始数据进行归一化;其次,采用主动形状模型(ASM)算法对三维人脸和二维人脸图像进行特征定位和特征分块;然后,利用基于分块主元分析(PCA)的稀疏形变模型算法实现每个人脸分块的三维重建;最后,实现了此算法在三维人脸识别中的应用。实验表明,此重建算法具有较高的精度和重建效率,还可以达到全局最优,并且可以提高三维人脸的识别率。     

Reconstructing 3D Shape,Albedo and Illumination from a Single Face Image

The morphable model has been employed to efficiently describe 3D face shape and the associated albedo with a reduced set of basis vectors. The spherical harmonics (SH) model provides a compact basis to well approximate the image appearance of a Lambertian object under different illumination conditions. Recently, the SH and morphable models have been integrated for 3D face shape reconstruction. However, the reconstructed 3D shape is either inconsistent with the SH bases or obtained just from landmarks only. In this work, we propose a geometrically consistent algorithm to reconstruct the 3D face shape and the associated albedo from a single face image iteratively by combining the morphable model and the SH model. The reconstructed 3D face geometry can uniquely determine the SH bases, therefore the optimal 3D face model can be obtained by minimizing the error between the input face image and a linear combination of the associated SH bases. In this way, we are able to preserve the consistency between the 3D geometry and the SH model, thus refining the 3D shape reconstruction recursively. Furthermore, we present a novel approach to recover the illumination condition from the estimated weighting vector for the SH bases in a constrained optimization formulation independent of the 3D geometry. Experimental results show the effectiveness and accuracy of the proposed face reconstruction and illumination estimation algorithm under different face poses and multiple‐light‐source illumination conditions.     

Driving 3D morphable models using shading cues

In this paper we show how surface orientation information inferred using shape-from-shading can be used to aid the process of fitting a 3D morphable model to an image of a face. We consider the problem of model dominance and show how shading constraints can be used to refine morphable model shape estimates, offering the possibility of exceeding the maximum possible accuracy of the model. We use this observation to motivate an optimisation scheme based on surface normal error. This ensures the fullest possible use of the information conveyed by the shading in an image. Moreover, our framework allows estimation of per-vertex albedo and bump maps which are not constrained to lie within the span of the model. This means the recovered model is capable of describing shape and reflectance phenomena not present in the training set. We show reconstruction and synthesis results and demonstrate that the shape and albedo estimates can be used for illumination insensitive recognition using only a single gallery image.     

形状约束的三维人脸组件形变模型

传统的三维人脸形变模型是通过对大量的三维人脸数据进行学习,构建描述人脸三维形状和纹理的参数模型,通过模型优化完成对二维人脸图像的三维重构。但是,实际中大量的训练样本是很难获得的,这导致形变模型描述能力的不完善,制约了它的应用。如将整个人脸看成由若干个组件组合而成,则在样本数不变的情况下降低了描述空间的维数,提高了模型的描述能力。但是在重构人脸图像时必须解决组件间三维空间的重叠合并,并且随着组件数目的增加,模型参数也随之增加,所以对优化算法也提出了更高的要求。为了解决形变模型的这些困难,提出了一种全局模型和组件模型的折中算法,即在形状上保持全局约束而纹理上进行组件匹配,从而在算法性能和算法复杂度之间获得了一个有效的平衡。     

Pose-Robust Facial Expression Recognition Using View-Based 2D $+$ 3D AAM

This paper proposes a pose-robust face tracking and facial expression recognition method using a view-based 2D 3D active appearance model (AAM) that extends the 2D 3D AAM to the view-based approach, where one independent face model is used for a specific view and an appropriate face model is selected for the input face image. Our extension has been conducted in many aspects. First, we use principal component analysis with missing data to construct the 2D 3D AAM due to the missing data in the posed face images. Second, we develop an effective model selection method that directly uses the estimated pose angle from the 2D 3D AAM, which makes face tracking pose-robust and feature extraction for facial expression recognition accurate. Third, we propose a double-layered generalized discriminant analysis (GDA) for facial expression recognition. Experimental results show the following: 1) The face tracking by the view-based 2D 3D AAM, which uses multiple face models with one face model per each view, is more robust to pose change than that by an integrated 2D 3D AAM, which uses an integrated face model for all three views; 2) the double-layered GDA extracts good features for facial expression recognition; and 3) the view-based 2D 3D AAM outperforms other existing models at pose-varying facial expression recognition.     

Self-adaptive morphable model based collaborative multi-view 3d face reconstruction in visual sensor network

3D face reconstruction is an efficient method for pedestrian recognition in non-cooperative environment because of its outstanding performance in robust face recognition for uncontrolled pose and illumination changes. Visual sensor network is widely used in target surveillance as powerful unattended distributed measurement systems. This paper proposes a collaborative multi-view non-cooperative 3D face reconstruction method in visual sensor network. A peer-to-peer paradigm-based visual sensor network is employed for distributed pedestrian tracking and optimal face image acquisition. Gaussian probability distribution-based multi-view data fusion is used for target localization, and kalman filter is applied for target tracking. A lightweight face image quality evaluation method is presented to search optimal face images. A self-adaptive morphable model is designed for multiview 3D face reconstruction. To adjust the self-adaptive morphable model, the optimal face images and their poses estimation are used. Cooperative chaotic particle swarm optimization is employed for parameters optimization of the self-adaptive morphable model. Experimental results on real data show that the proposed method can acquire optimal face images and achieve non-cooperative 3D reconstruction efficiently.     

Efficient constrained local model fitting for non-rigid face alignment

Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The “simultaneous” algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2–3 fps). The “project-out” algorithm for fitting an AAM achieves faster than real time performance (>200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the “simultaneous” AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the “exhaustive local search” (ELS) algorithm. Experiments were conducted on the CMU MultiPIE database.     

Pose robust face tracking by combining view-based AAMs and temporal filters

Active appearance models (AAMs) are useful for face tracking for the advantages of detailed face interpretation, accurate alignment and high efficiency. However, they are sensitive to initial parameters and may easily be stuck in local minima due to the gradient-descent optimization, which makes the AAM based face tracker unstable in the presence of large pose deviation and fast motion. In this paper, we propose to combine the view-based AAMs with two novel temporal filters to overcome the limitations. First, we build a new view space based on the shape parameters of AAMs, instead of the model parameters controlling both the shape and appearance, for the purpose of pose estimation. Then the Kalman filter is used to simultaneously update the pose and shape parameters for a better fitting of each frame. Second, we propose a temporal matching filter which is twofold. The inter-frame local appearance constraint is incorporated into AAM fitting, where the mechanism of the active shape model (ASM) is also implemented in a unified framework to find more accurate matching points. Moreover, we propose to initialize the shape with correspondences found by a random forest based local feature matching. By introducing the local information and temporal correspondences, the twofold temporal matching filter improves the tracking stability when confronted with fast appearance changes. Experimental results show that our algorithm is more pose robust than basic AAMs and some state-of-art AAM based methods, and that it can also handle large expressions and non-extreme illumination changes in test video sequences.     

Efficient generic face model fitting to images and videos

In this paper we present a robust and lightweight method for the automatic fitting of deformable 3D face models on facial images. Popular fitting techniques such as those based on statistical models of shape and appearance require a training stage based on a set of facial images and their corresponding facial landmarks, which have to be manually labeled. Therefore, new images in which to fit the model cannot differ too much in shape and appearance (including illumination variation, facial hair, wrinkles, etc.) from those used for training. By contrast, our approach can fit a generic face model in two steps: (1) the detection of facial features based on local image gradient analysis and (2) the backprojection of a deformable 3D face model through the optimization of its deformation parameters. The proposed approach can retain the advantages of both learning-free and learning-based approaches. Thus, we can estimate the position, orientation, shape and actions of faces, and initialize user-specific face tracking approaches, such as Online Appearance Models (OAMs), which have shown to be more robust than generic user tracking approaches. Experimental results show that our method outperforms other fitting alternatives under challenging illumination conditions and with a computational cost that allows its implementation in devices with low hardware specifications, such as smartphones and tablets. Our proposed approach lends itself nicely to many frameworks addressing semantic inference in face images and videos.     

The asymmetry of image registration and its application to face tracking

Most image registration problems are formulated in an asymmetric fashion. Given a pair of images, one is implicitly or explicitly regarded as a template and warped onto the other to match as well as possible. In this paper, we focus on this seemingly arbitrary choice of the roles and reveal how it may lead to biased warp estimates in the presence of relative scaling. We present a principled way of selecting the template and explain why only the correct asymmetric form, with the potential inclusion of a blurring step, can yield an unbiased estimator. We validate our analysis in the domain of model-based face tracking. We show how the usual active appearance model (AAM) formulation overlooks the asymmetry issue, causing the fitting accuracy to degrade quickly when the observed objects are smaller than their model. We formulate a novel, "resolution-aware fitting" (RAF) algorithm that respects the asymmetry and incorporates an explicit model of the blur caused by the camera's sensing elements into the fitting formulation. We compare the RAF algorithm against a state-of-the-art tracker across a variety of resolutions and AAM complexity levels. Experimental results show that RAF significantly improves the estimation accuracy of both shape and appearance parameters when fitting to low-resolution data. Recognizing and accounting for the asymmetry of image registration leads to tangible accuracy improvements in analyzing low-resolution imagery     

结合特征适配与拉普拉斯形变的3维人脸重建

目的 数字娱乐产业的发展要求3维人脸重建技术能重建高分辨率3维人脸,并具有较高计算效率和重建准确性。针对这一情况,提出一种基于单幅图像的高分辨率3维人脸重建方法。方法 该方法包含特征适配与拉普拉斯形变两部分。预先用1组3维人脸样本上的3维特征构造可变形模型。给定图像时,从其上自动提取2维特征点,并根据获得问题最优解的必要条件进行特征适配以重建个性化3维特征;然后基于拉普拉斯方法,用该3维特征对一般人脸模型进行变形以获得特定高分辨率3维人脸;最后通过纹理合成获得真实感人脸。结果 用本文方法和已有方法分别进行可变形模型适配和模型变形,本文的特征适配方法具有更快的收敛速度和更高的准确性,拉普拉斯方法具有更小的重建误差。纹理映射后的3维人脸具有很好的视觉效果。结论 本文方法将特征适配与拉普拉斯形变结合起来进行高分辨率3维人脸重建。实验结果表明所提出的方法具有较高的计算效率和准确性,能实现较为理想的高分辨率3维人脸重建。     

Reflectance from images: a model-based approach for human faces

In this paper, we present an image-based framework that acquires the reflectance properties of a human face. A range scan of the face is not required. Based on a morphable face model, the system estimates the 3D shape and establishes point-to-point correspondence across images taken from different viewpoints and across different individuals' faces. This provides a common parameterization of all reconstructed surfaces that can be used to compare and transfer BRDF data between different faces. Shape estimation from images compensates deformations of the face during the measurement process, such as facial expressions. In the common parameterization, regions of homogeneous materials on the face surface can be defined a priori. We apply analytical BRDF models to express the reflectance properties of each region and we estimate their parameters in a least-squares fit from the image data. For each of the surface points, the diffuse component of the BRDF is locally refined, which provides high detail. We present results for multiple analytical BRDF models, rendered at novel orientations and lighting conditions.