Measuring 3D face deformations from RGB images of expression rehabilitation exercises

Background The accurate(quantitative)analysis of 3D face deformation is a problem of increasing interest in many applications.In particular,defining a 3D model of the face deformation into a 2D target image to capture local and asymmetric deformations remains a challenge in existing literature.A measure of such local deformations may be a relevant index for monitoring the rehabilitation exercises of patients suffering from Par-kinson’s or Alzheimer’s disease or those recovering from a stroke.Methods In this paper,a complete framework that allows the construction of a 3D morphable shape model(3DMM)of the face is presented for fitting to a target RGB image.The model has the specific characteristic of being based on localized components of deformation.The fitting transformation is performed from 3D to 2D and guided by the correspondence between landmarks detected in the target image and those manually annotated on the average 3DMM.The fitting also has the distinction of being performed in two steps to disentangle face deformations related to the identity of the target subject from those induced by facial actions.Results The method was experimentally validated using the MICC-3D dataset,which includes 11 subjects.Each subject was imaged in one neutral pose and while performing 18 facial actions that deform the face in localized and asymmetric ways.For each acquisition,3DMM was fit to an RGB frame whereby,from the apex facial action and the neutral frame,the extent of the deformation was computed.The results indicate that the proposed approach can accurately capture face deformation,even localized and asymmetric deformations.Conclusion The proposed framework demonstrated that it is possible to measure deformations of a reconstructed 3D face model to monitor facial actions performed in response to a set of targets.Interestingly,these results were obtained using only RGB targets,without the need for 3D scans captured with costly devices.This paves the way for the use of the proposed tool in remote medical rehabilitation monitoring.

Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

3D morphable models(3DMMs)are generative models for face shape and appearance.Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain consistent.However,the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution.In contrast,the identity embeddings meet the hypersphere distribution,and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously.In other words,recognition loss and reconstruction loss can not decrease jointly due to their conflict distribution.To address this issue,we propose the Sphere Face Model(SFM),a novel 3DMM for monocular face reconstruction,preserving both shape fidelity and identity consistency.The core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes,and the basic matrix is learned by adopting a twostage training approach where 3D and 2D training data are used in the first and second stages,respectively.We design a novel loss to resolve the distribution mismatch,enforcing that the shape parameters have the hyperspherical distribution.Our model accepts 2D and 3D data for constructing the sphere face models.Extensive experiments show that SFM has high representation ability and clustering performance in its shape parameter space.Moreover,it produces highfidelity face shapes consistently in challenging conditions in monocular face reconstruction.The code will be released at https://github.com/a686432/SIR.

3D Face Similarity Measure by Frechet Distances of Geodesics

3D face similarity is a critical issue in computer vision, computer graphics and face recognition and so on. Since Fr@chet distance is an effective metric for measuring curve similarity, a novel 3D face similarity measure method based on Fr^chet distances of geodesics is proposed in this paper. In our method, the surface similarity between two 3D faces is measured by the similarity between two sets of 3D curves on them. Due to the intrinsic property of geodesics, we select geodesics as the comparison curves. Firstly, the geodesics on each 3D facial model emanating from the nose tip point are extracted in the same initial direction with equal angular increment. Secondly, the Fr@chet distances between the two sets of geodesics on the two compared facial models are computed. At last, the similarity between the two facial models is computed based on the Fr6chet distances of the geodesics obtained in the second step. We verify our method both theoretically and practically. In theory, we prove that the similarity of our method satisfies three properties： reflexivity, symmetry, and triangle inequality. And in practice, experiments are conducted on the open 3D face database GavaDB, Texas 3D Face Recognition database, and our 3D face database. After the comparison with iso-geodesic and Hausdorff distance method, the results illustrate that our method has good discrimination ability and can not only identify the facial models of the same person, but also distinguish the facial models of any two different persons.

Genome-wide variants of Eurasian facial shape differentiation and a prospective model of DNA based face prediction

It is a long-standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics, Genome-wide associ- ation studies （GWAS） were conducted on a discovery panel of Uyghurs, Six significant loci were iden- tified, four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A prospective model was also developed to predict 3D faces based on top GWAS signals and tested in hypothetic forensic scenarios.

基于逆向工程的呼吸面罩设计

提出了利用逆向工程技术进行呼吸面罩个性化设计的方法.利用逆向工程方法获得了三维面部模型,基于人体面部的逆向造型提取出面部的特征点与特征曲线,并就典型的特征曲线进行曲面设计,完成呼吸面罩的造型设计.利用Surfacer与Pro/Engineer软件实现了特定人脸呼吸面罩的造型设计.

自动三维度量学及在人体头面部测量中的应用

本文叙述一种新的无需参考物体或平面的自动三维度量学测量系统的原理,以及应用这种系统对人体头面部进行测量。

光学测量系统在面部外形测量中的应用研究

目的研究面部三维光学测量系统在面部外形重建和测量中的实用性和可靠性。方法利用自行研制的面部三维光学测量系统扫描45名正常人面形,结合Geomagic studio软件反求重建面部外形。比较面部内眦间距、鼻长、鼻宽、鼻高、鼻深等的反求测量结果与手工测量结果间的差异。结果基于面部三维光学测量系统结合反求软件直接重建了45名正常人的面部三维外形,面部测量项目的反求测量结果与手工测量结果在统计学上无明显差异(P>0.05)。结论面部三维光学测量系统在面部外形采集、反求重建、三维测量中具有较高的可靠性和较好的实用性。

三维面部表情识别技术的研究进展

三维采集设备的快速发展,极大推动了三维数据技术的研究。其中,以三维人脸数据为载体的三维面部表情识别研究成果不断涌现。三维面部表情识别可以极大克服二维识别中的姿态和光照变化等方面问题。对三维表情识别技术进行了系统概括,尤其针对三维表情的关键技术,即对表情特征提取、表情编码分类及表情数据库进行了总结分析,并提出了三维表情识别的研究建议。三维面部表情识别技术在识别率上基本满足要求,但实时性上需要进一步优化。相关内容对该领域的研究具有指导意义。

应用相位测量轮廓技术重建面部睁眼三维数字化模型

目的:建立活体睁眼状态下的面部三维数字化模型,为颌面缺损的仿真修复奠定基础。方法:采用3D Ca-Maga人体三维彩色数字化系统对面部进行活体测量,获得三维点云数据,运用CloudForm反求工程软件对数据进行处理后,转化为相应的数字化文件格式,在Geomagic实体构造软件中重建面部三维数字化模型。结果:成功建立了面部睁眼状态下的三维数字化模型。结论:应用相位测量轮廓术可快速准确安全的重建真实的面部三维形态,该方法在颜面赝复、口腔颌面外科、正畸科、整形外科都将有广阔的应用前景。

实时交互式三维脸部表情动画研究及其实现

根据人脸的生理结构特点,使用分块造型技术进行脸部建模,提出了一个基于“脸型特征”和“五官特征”的参数化肌肉模型,通过模型上控制顶点的属性值来控制脸部形成不同的表情,并利用MAYA MEL语言实现了一个实时交互操作界面。这使得对脸部表情的研究更加方便、灵活,建模及其动画化也更加逼真、实用。