Pose determination of human faces by using vanishing points

A new method for estimating 3D-head pose from a monocular image is proposed in this paper. The approach employs general prior knowledge of face structure and the corresponding geometrical constraints provided by the location of vanishing point to determine pose of human faces. The connection of the two far-eye corners and the two neighboring-eye corners, respectively, form the eye-lines. Connecting the two far-mouth corners forms the mouth-line. The eye-lines and the mouth-line are assumed parallel in 3D space, and their vanishing point on the image plane can be used to infer 3D pose of the human face. Perspective projection imaging model is used and an accurate analytic solution of pose (position and orientation) of human face is deduced. The orientation of the facial plane can be obtained when the ratio of the lengths of the eye-line segment (far-eye corners) and the mouth-line segment (far-mouth corners) is known. Furthermore, if one of the two lengths is known, then the absolute positions of the feature corners can be located. The robustness analysis of the algorithm with synthetic data and the experimental results of real face images are enclosed.     

基于单幅图像的人脸三维姿态估计

针对单张人像的三维姿态计算,结合面貌测量和射影几何的理论提出了一种方法：首先在人面部的平面区域内,选取眼角点,口角点,鼻翼点建立人脸模型;然后根据人脸平面上两个相互垂直的特征线投影到照片上的灭点位置,求出人脸平面的旋转方向。该方法特征点易于标定,且无需任何的辅助设备和先验知识,具有一定的实用性。     

Passive Three Dimensional Face Recognition Using Iso-Geodesic Contours and Procrustes Analysis

We introduce a new model for personal recognition based on the 3-D geometry of the face. The model is designed for application scenarios where the acquisition conditions constrain the facial position. The 3-D structure of a facial surface is compactly represented by sets of contours (facial contours) extracted around automatically pinpointed nose tip and inner eye corners. The metric used to decide whether a point on the face belongs to a facial contour is its geodesic distance from a given landmark. Iso-geodesic contours are inherently robust to head pose variations, including in-depth rotations of the face. Since these contours are extracted from rigid parts of the face, the resulting recognition algorithms are insensitive to changes in facial expressions. The facial contours are encoded using innovative pose invariant features, including Procrustean distances defined on pose-invariant curves. The extracted features are combined in a hierarchical manner to create three parallel face recognizers. Inspired by the effectiveness of region ensembles approaches, the three recognizers constructed around the nose tip and inner corners of the eyes are fused both at the feature-level and the match score-level to create a unified face recognition algorithm with boosted performance. The performances of the proposed algorithms are evaluated and compared with other algorithms from the literature on a large public database appropriate for the assumed constrained application scenario.     

Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D model registration

Registering a 3D facial model onto a 2D image is important for constructing pixel-wise correspondences between different facial images. The registration is based on a 3 \(\times \) 4 dimensional projection matrix, which is obtained from pose estimation. Conventional pose estimation approaches employ facial landmarks to determine the coefficients inside the projection matrix and are sensitive to missing or incorrect landmarks. In this paper, a landmark-free pose estimation method is presented. The method can be used to estimate the matrix when facial landmarks are not available. Experimental results show that the proposed method outperforms several landmark-free pose estimation methods and achieves competitive accuracy in terms of estimating pose parameters. The method is also demonstrated to be effective as part of a 3D-aided face recognition pipeline (UR2D), whose rank-1 identification rate is competitive to the methods that use landmarks to estimate head pose.     

New algorithm for 3D facial model reconstruction and its application in virtual reality

3D human face model reconstruction is essential to the generation of facial animations that is widely used in the field of virtual reality (VR). The main issues of 3D facial model reconstruction based on images by vision technologies are in twofold: one is to select and match the corresponding features of face from two images with minimal interaction and the other is to generate the realistic-looking human face model. In this paper, a new algorithm for realistic-looking face reconstruction is presented based on stereo vision. Firstly, a pattern is printed and attached to a planar surface for camera calibration, and corners generation and corners matching between two images are performed by integrating modified image pyramid Lucas-Kanade (PLK) algorithm and local adjustment algorithm, and then 3D coordinates of corners are obtained by 3D reconstruction. Individual face model is generated by the deformation of general 3D model and interpolation of the features. Finally, realistic-looking human face model     

A study on the dual vanishing point property

Vanishing points and vanishing lines are useful information in computer vision. In this study, an interesting dual property of vanishing point is first introduced. Next, we point out that there also exists a dual property of vanishing line. With the dual vanishing point and vanishing line properties, some 3D intersection inference can be made based on their image lines. Two applications are given to illustrate the usage of the new results. The first one is to derive the 3D pose determination of a circle using two parallel image lines. The second one uses six specially designed 3D lines to adjust the cameras with respect to a fixture in a binocular vision system such that the resultant camera coordinate axes become parallel.     

基于面部特征点的单幅图像人脸姿态估计方法

针对目前基于学习的姿态估计方法对训练样本及设备要求较高的问题,提出一种基于面部特征点定位的无需训练即能估计单幅图像中人脸姿态的方法。通过Adrian Bulat人脸特征点定位器和Candide-3构建稀疏通用人脸模型并获得五官特征点,确定模型绕Z轴的旋转范围及搜索步长,在指定Z轴旋转角度下,使用修正牛顿法通过模型的旋转、平移及缩放变换对齐模型和图像中人脸五官角点,得到该角度下模型绕X轴、Y轴的旋转角度及绕Z轴候选角度下的损失函数值,根据最小损失函数值确定人脸绕3个轴旋转的最佳值。实验结果表明,该方法能够快速估计自遮挡的大姿态角度人脸,在公共人脸库Multi-PIE、BIWI和AFLW上的平均误差分别为3.79°、4.37°和6.04°,明显高于同类人脸姿态估计算法,具有较好的实用性能。     

Recovering facial pose with the EM algorithm

This paper describes how 3D facial pose may be estimated by fitting a template to 2D feature locations. The fitting process is realised as projecting the control points of a 3D template onto the 2D feature locations under orthographic projection. The parameters of the orthographic projection are iteratively estimated using the EM algorithm. The method is evaluated on both contrived data with known ground-truth together with some more naturalistic imagery. These experiments reveal that under favourable conditions the algorithm can estimate facial pitch to within 3°.     

3D通用人脸模型的自适应算法及应用

利用3D人脸建模的方法进行人脸识别有效地克服了2D人脸识别系统中识别率易受光照、姿态、表情影响的缺陷。文章采用一种依据人脸图像对3D通用人脸模型进行自适应调整的有效算法,构造出特定的人脸模型并运用于人脸识别中。通过比较从人脸图像中估算出的特征点与通用人脸模型在图像平面上的投影点之间的关系,对3D通用人脸模型进行全局和局部调整,以适应人脸中眼、口、鼻的个性化特征。最后以一个实例说明了此算法的应用。     

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.     

Automatic interpretation and coding of face images using flexiblemodels

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above     

深度学习人脸特征点自动定位综述

人脸特征点定位是根据输入的人脸数据自动定位出预先按人脸生理特征定义的眼角、鼻尖、嘴角和脸部轮廓等面部关键特征点,在人脸识别和分析等系统中起着至关重要的作用。本文对基于深度学习的人脸特征点自动定位进行综述,阐释了人脸特征点自动定位的含义,归纳了目前常用的人脸公开数据集,系统阐述了针对2维和3维数据特征点的自动定位方法,总结了各方法的研究现状及其应用,分析了当前人脸特征点自动定位技术在深度学习应用中的现状、存在问题及发展趋势。在公开的2维和3维人脸数据集上对不同方法进行了比较。通过研究可以看出,基于深度学习的2维人脸特征点的自动定位方法研究相对比较深入,而3维人脸特征点定位方法的研究在模型表示、处理方法和样本数量上都存在挑战。未来基于深度学习的3维人脸特征点定位方法将成为研究趋势。     

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。     

2D representation of facial surfaces for multi-pose 3D face recognition

The increasing availability of 3D facial data offers the potential to overcome the intrinsic difficulties faced by conventional face recognition using 2D images. Instead of extending 2D recognition algorithms for 3D purpose, this letter proposes a novel strategy for 3D face recognition from the perspective of representing each 3D facial surface with a 2D attribute image and taking the advantage of the advances in 2D face recognition. In our approach, each 3D facial surface is mapped homeomorphically onto a 2D lattice, where the value at each site is an attribute that represents the local 3D geometrical or textural properties on the surface, therefore invariant to pose changes. This lattice is then interpolated to generate a 2D attribute image. 3D face recognition can be achieved by applying the traditional 2D face recognition techniques to obtained attribute images. In this study, we chose the pose invariant local mean curvature calculated at each vertex on the 3D facial surface to construct the 2D attribute image and adopted the eigenface algorithm for attribute image recognition. We compared our approach to state-of-the-art 3D face recognition algorithms in the FRGC (Version 2.0), GavabDB and NPU3D database. Our results show that the proposed approach has improved the robustness to head pose variation and can produce more accurate 3D multi-pose face recognition.     

Projection matrix by orthogonal vanishing points

Calculation of camera projection matrix, also called camera calibration, is an essential task in many computer vision and 3D data processing applications. Calculation of projection matrix using vanishing points and vanishing lines is well suited in the literature; where the intersection of parallel lines (in 3D Euclidean space) when projected on the camera image plane (by a perspective transformation) is called vanishing point and the intersection of two vanishing points (in the image plane) is called vanishing line. The aim of this paper is to propose a new formulation for easily computing the projection matrix based on three orthogonal vanishing points. It can also be used to calculate the intrinsic and extrinsic camera parameters. The proposed method reaches to a closed-form solution by considering only two feasible constraints of zero-skewness in the internal camera matrix and having two corresponding points between the world and the image. A nonlinear optimization procedure is proposed to enhance the computed camera parameters, especially when the measurement error of input parameters or the skew factor are not negligible. The proposed method has been run on real and synthetic data for more precise evaluations. The provided experimental results demonstrate the superiority of the proposed method.     

快速的人脸轮廓检测及姿态估计算法

提出一种基于人脸特征区域划分的人脸轮廓检测方法和快速人脸姿态估计方法.该方法根据特征点在人脸的分布情况将人脸划分为9个区域.对于每个选定的区域,首先检测出其初始轮廓线,然后用三次多项式对其进行曲线拟合处理,最后把不同区域的轮廓线连接起来得到完整的人脸轮廓.此外,为了快速、准确地估计出人脸的姿态,本文从人脸的对称性出发,提出了进行人脸姿态估计的面积模型和近似平面模型.实验表明,本文所提出的轮廓检测方法对于复杂背景中具有不同姿态的人脸图像可以得到较满意的检测结果.和其它检测方法相比,本文方法具有模型简单、计算速度快等优点.     

综合鲁棒特征和在线学习的自适应三维人脸多特征跟踪

提出一种灰度与边强度信息相结合的鲁棒特征并综合在线学习方法来进行自适应视频人脸多特征跟踪.算法思想是利用三维参数化网格模型对人脸及表情进行建模,利用弱透视模型对头部姿态建模,求取归一化后的形状无关灰度和边强度纹理组合成一种鲁棒特征,建立单高斯自适应纹理模型,并采用梯度下降迭代算法进行模型匹配得到姿态和表情参数.实验证明,本方法比单纯利用灰度特征在复杂光线和表情下具有更好的鲁棒性.     

Real-Time 3D Head Pose Tracking Through 2.5D Constrained Local Models with Local Neural Fields

Tracking the head in a video stream is a common thread seen within computer vision literature, supplying the research community with a large number of challenging and interesting problems. Head pose estimation from monocular cameras is often considered an extended application after the face tracking task has already been performed. This often involves passing the resultant 2D data through a simpler algorithm that best fits the data to a static 3D model to determine the 3D pose estimate. This work describes the 2.5D constrained local model, combining a deformable 3D shape point model with 2D texture information to provide direct estimation of the pose parameters, avoiding the need for additional optimization strategies. It achieves this through an analytical derivation of a Jacobian matrix describing how changes in the parameters of the model create changes in the shape within the image through a full-perspective camera model. In addition, the model has very low computational complexity and can run in real-time on modern mobile devices such as tablets and laptops. The point distribution model of the face is built in a unique way, so as to minimize the effect of changes in facial expressions on the estimated head pose and hence make the solution more robust. Finally, the texture information is trained via local neural fields—a deep learning approach that utilizes small discriminative patches to exploit spatial relationships between the pixels and provide strong peaks at the optimal locations.

     

多信息融合的多姿态三维人脸面部五官标志点定位方法

针对三维人脸模型面部五官标志点定位对姿态变化非常敏感的问题,提出了一种基于多信息融合的多姿态三维人脸五官标志点定位方法.首先对二维人脸纹理图像采用仿射不变的Affine- SIFT方法进行特征点检测,再利用映射关系将其投影到三维空间,并采用局部邻域曲率变化最大规则和迭代约束优化相结合的方法对面部五官标志点进行精确定位.在FRGC2.0和自建NPU3D数据库的实验结果表明,文中方法无需对姿态和三维数据的格式进行预先估计和定义,算法复杂度低,同时对人脸模型的姿态有着较强的鲁棒性,与现有五官标志点定位方法相比,有着更高的定位精度.     

三维人脸识别研究综述

近二十多年来,虽然基于图像的人脸识别已取得很大进展,并可在约束环境下获得很好的识别性能,但仍受光照、姿态、表情等变化的影响很大,其本质原因在于图像是三维物体在二维空间的简约投影.因此,利用脸部曲面的显式三维表达进行人脸识别正成为近几年学术界的研究热点.文中分析了三维人脸识别的产生动机、概念与基本过程;根据特征形式,将三维人脸识别算法分为基于空域直接匹配、基于局部特征匹配、基于整体特征匹配三大类进行综述;对二维和三维的双模态融合方法进行分类阐述;列出了部分代表性的三维人脸数据库;对部分方法进行实验比较,并分析了方法有效性的原因;总结了目前三维人脸识别技术的优势与困难,并探讨了未来的研究趋势.