Recognition of 3D facial expression dynamics

In this paper we propose a method that exploits 3D motion-based features between frames of 3D facial geometry sequences for dynamic facial expression recognition. An expressive sequence is modelled to contain an onset followed by an apex and an offset. Feature selection methods are applied in order to extract features for each of the onset and offset segments of the expression. These features are then used to train GentleBoost classifiers and build a Hidden Markov Model in order to model the full temporal dynamics of the expression. The proposed fully automatic system was employed on the BU-4DFE database for distinguishing between the six universal expressions: Happy, Sad, Angry, Disgust, Surprise and Fear. Comparisons with a similar 2D system based on the motion extracted from facial intensity images was also performed. The attained results suggest that the use of the 3D information does indeed improve the recognition accuracy when compared to the 2D data in a fully automatic manner.     

对表情鲁棒的面部轮廓线3维人脸识别

目的表情变化是3维人脸识别面临的主要问题。为克服表情影响,提出了一种基于面部轮廓线对表情鲁棒的3维人脸识别方法。方法首先,对人脸进行预处理,包括人脸区域切割、平滑处理和姿态归一化,将所有的人脸置于姿态坐标系下;然后,从3维人脸模型的半刚性区域提取人脸多条垂直方向的轮廓线来表征人脸面部曲面;最后,利用弹性曲线匹配算法计算不同3维人脸模型间对应的轮廓线在预形状空间(preshape space)中的测地距离,将其作为相似性度量,并且对所有轮廓线的相似度向量加权融合,得到总相似度用于分类。结果在FRGC v2.0数据库上进行识别实验,获得97.1%的Rank-1识别率。结论基于面部轮廓线的3维人脸识别方法,通过从人脸的半刚性区域提取多条面部轮廓线来表征人脸,在一定程度上削弱了表情的影响,同时还提高了人脸匹配速度。实验结果表明,该方法具有较强的识别性能,并且对表情变化具有较好的鲁棒性。     

Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis

3D face scans have been widely used for face modeling and analysis. Due to the fact that face scans provide variable point clouds across frames, they may not capture complete facial data or miss point-to-point correspondences across various facial scans, thus causing difficulties to use such data for analysis. This paper presents an efficient approach to representing facial shapes from face scans through the reconstruction of face models based on regional information and a generic model. A new approach for 3D feature detection and a hybrid approach using two vertex mapping algorithms, displacement mapping and point-to-surface mapping, and a regional blending algorithm are proposed to reconstruct the facial surface detail. The resulting models can represent individual facial shapes consistently and adaptively, establishing facial point correspondences across individual models. The accuracy of the generated models is evaluated quantitatively. The applicability of the models is validated through the application of 3D facial expression recognition using the static 3DFE and dynamic 4DFE databases. A comparison with the state of the art has also been reported.     

保角特征结合改进差分进化算法的三维人脸识别

针对三维人脸识别的高复杂度和二维人脸识别无法提供粒状线索的问题,提出一种全自动3D人脸表情识别算法, 该算法主要是提供比2D人脸识别更多的线索,同时降低计算复杂度。首先通过保角映射将3D人脸转化到2D平面,保留了面部变化的线索;然后,提出了基于优化算法的差分进化(DE)算法用于提高识别效率,同时提取最优人脸特征集和分类器参数,加速鲁棒特征(SURF)池描述了所有预期的人脸特征点。在博斯普鲁斯、FRGC v2及自己搜集的人脸数据集上的实验结果表明,本文算法解决了三维人脸识别的高计算复杂度和二维人脸识别的线索低问题,并在不降低识别性能的前提下大大地节约了成本,相比几种较为先进的三维人脸识别算法,本文算法取得了更好的识别效果,有望应用于一些商业人脸识别系统。     

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。     

结合加权局部旋度模式的3维人脸表情识别

目的 3维人脸的表情信息不均匀地分布在五官及脸颊附近,对表情进行充分的描述和合理的权重分配是提升识别效果的重要途径。为提高3维人脸表情识别的准确率,提出了一种基于带权重局部旋度模式的3维人脸表情识别算法。方法 首先,为了提取具有较强表情分辨能力的特征,提出对3维人脸的旋度向量进行编码,获取局部旋度模式作为表情特征;然后,提出将ICNP（interactive closest normal points）算法与最小投影偏差算法结合,前者实现3维人脸子区域的不规则划分,划分得到的11个子区域保留了表情变化下面部五官和肌肉的完整性,后者根据各区域对表情识别的贡献大小为各区域的局部旋度模式特征分配权重;最后,带有权重的局部旋度模式特征被输入到分类器中实现表情识别。结果 基于BU-3DFE 3维人脸表情库对本文提出的局部旋度模式特征进行评估,结果表明其分辨能力较其他表情特征更强;基于BU-3DFE库进行表情识别实验,与其他3维人脸表情识别算法相比,本文算法取得了最高的平均识别率,达到89.67%,同时对易混淆的“悲伤”、“愤怒”和“厌恶”等表情的误判率也较低。结论 局部旋度模式特征对3维人脸的表情有较强的表征能力; ICNP算法与最小投影偏差算法的结合,能够实现区域的有效划分和权重的准确计算,有效提高特征对表情的识别能力。试验结果表明本文算法对3维人脸表情具有较高的识别率,并对易混淆的相似表情仍具有较好的识别效果。     

3D/4D facial expression analysis: An advanced annotated face model approach

Facial expression analysis has interested many researchers in the past decade due to its potential applications in various fields such as human–computer interaction, psychological studies, and facial animation. Three-dimensional facial data has been proven to be insensitive to illumination condition and head pose, and has hence gathered attention in recent years. In this paper, we focus on discrete expression classification using 3D data from the human face. The paper is divided in two parts. In the first part, we present improvement to the fitting of the Annotated Face Model (AFM) so that a dense point correspondence can be found in terms of both position and semantics among static 3D face scans or frames in 3D face sequences. Then, an expression recognition framework on static 3D images is presented. It is based on a Point Distribution Model (PDM) which can be built on different features. In the second part of this article, a systematic pipeline that operates on dynamic 3D sequences (4D datasets or 3D videos) is proposed and alternative modules are investigated as a comparative study. We evaluated both 3D and 4D Facial Expression Recognition pipelines on two publicly available facial expression databases and obtained promising results.     

基于深度数据的空间人脸旋转角度估计

提出一种基于三维人脸深度数据的人脸姿态计算方法。利用人脸的深度数 据以及与其一一对应的灰度图像,根据微分几何原理和相应的曲率算法与人脸数据中的灰度 特征对人脸面部关键特征点定位,进而计算出人脸姿态在三维空间中的3 个姿态角。实验证 明该方法能在姿态变化情况下实现对人脸旋转角的准确估计,为进一步的人脸识别和表情分 析提供基础。     

Shape analysis of local facial patches for 3D facial expression recognition

In this paper we address the problem of 3D facial expression recognition. We propose a local geometric shape analysis of facial surfaces coupled with machine learning techniques for expression classification. A computation of the length of the geodesic path between corresponding patches, using a Riemannian framework, in a shape space provides a quantitative information about their similarities. These measures are then used as inputs to several classification methods. The experimental results demonstrate the effectiveness of the proposed approach. Using multiboosting and support vector machines (SVM) classifiers, we achieved 98.81% and 97.75% recognition average rates, respectively, for recognition of the six prototypical facial expressions on BU-3DFE database. A comparative study using the same experimental setting shows that the suggested approach outperforms previous work.     

Exploring the effect of illumination on automatic expression recognition using the ICT-3DRFE database

One of the main challenges in facial expression recognition is illumination invariance. Our long-term goal is to develop a system for automatic facial expression recognition that is robust to light variations. In this paper, we introduce a novel 3D Relightable Facial Expression (ICT-3DRFE) database that enables experimentation in the fields of both computer graphics and computer vision. The database contains 3D models for 23 subjects and 15 expressions, as well as photometric information that allow for photorealistic rendering. It is also facial action units annotated, using FACS standards. Using the ICT-3DRFE database we create an image set of different expressions/illuminations to study the effect of illumination on automatic expression recognition. We compared the output scores from automatic recognition with expert FACS annotations and found that they agree when the illumination is uniform. Our results show that the output distribution of the automatic recognition can change significantly with light variations and sometimes causes the discrimination of two different expressions to be diminished. We propose a ratio-based light transfer method, to factor out unwanted illuminations from given images and show that it reduces the effect of illumination on expression recognition.     

三维人脸捕获建模和动画技术综述

从历史发展和当前的研究现状的角度对三维人脸建模和动画算法进行综述,通过对三维捕获、参数化人脸建模和动画、表情克隆等问题及其典型算法的基本原理、优缺点等的分析,对它们的特性进行描述和比较.展望进一步值得研究的问题和方向.以期读者对三维人脸建模和动画的主流技术有较全面的了解,并对未来的研究工作有所帮助.     

面部表情分析进展和挑战

面部表情分析是计算机通过分析人脸信息尝试理解人类情感的一种技术,目前已成为计算机视觉领域的热点话题。其挑战在于数据标注困难、多人标签一致性差、自然环境下人脸姿态大以及遮挡等。为了推动面部表情分析发展,本文概述了面部表情分析的相关任务、进展、挑战和未来趋势。首先,简述了面部表情分析的几个常见任务、基本算法框架和数据库;其次,对人脸表情识别方法进行了综述,包括传统的特征设计方法以及深度学习方法;接着,对人脸表情识别存在的问题与挑战进行总结思考;最后,讨论了未来发展趋势。通过全面综述和讨论,总结以下观点：1）针对可靠人脸表情数据库规模小的问题,从人脸识别模型进行迁移学习以及利用无标签数据进行半监督学习是两个重要策略;2）受模糊表情、低质量图像以及标注者的主观性影响,非受控自然场景的人脸表情数据的标签库存在一定的不确定性,抑制这些因素可以使得深度网络学习真正的表情特征;3）针对人脸遮挡和大姿态问题,利用局部块进行融合的策略是一个有效的策略,另一个值得考虑的策略是先在大规模人脸识别数据库中学习一个对遮挡和姿态鲁棒的模型,再进行人脸表情识别迁移学习;4）由于基于深度学习的表情识别方法受很多超参数影响,导致当前人脸表情识别方法的可比性不强,不同的表情识别方法有必要在不同的简单基线方法上进行评测。目前,虽然非受控自然环境下的表情分析得到较快发展,但是上述问题和挑战仍然有待解决。人脸表情分析是一个比较实用的任务,未来发展除了要讨论方法的精度也要关注方法的耗时以及存储消耗,也可以考虑用非受控环境下高精度的人脸运动单元检测结果进行表情类别推断。     

Efficient 3D face recognition handling facial expression and hair occlusion

This paper presents an efficient 3D face recognition method to handle facial expression and hair occlusion. The proposed method uses facial curves to form a rejection classifier and produce a facial deformation mapping and then adaptively selects regions for matching. When a new 3D face with an arbitrary pose and expression is queried, the pose is normalized based on the automatically detected nose tip and the principal component analysis (PCA) follows. Then, the facial curve in the nose region is extracted and used to form the rejection classifier which quickly eliminates dissimilar faces in the gallery for efficient recognition. Next, six facial regions which cover the face are segmented and curves in these regions are used to map facial deformation. Regions used for matching are automatically selected based on the deformation mapping. In the end, results of all the matching engines are fused by weighted sum rule. The approach is applied on the FRGC v2.0 dataset and a verification rate of 96.0% for ROC III is achieved as a false acceptance rate (FAR) of 0.1%. In the identification scenario, a rank-one accuracy of 97.8% is achieved.     

基于双线性模型的人脸表情识别技术

针对现阶段人脸表情识别过程中所遇到的问题,基于三维数据库BU-3DFE中的三维表情数据,研究三维人脸表情数据的点云对齐及基于对齐数据的双线性模型建立,对基于双线性模型的识别算法加以改进,形成新的识别分类算法,降低原有算法中身份特征参与计算的比重,最大可能地降低身份特征对于整个表情识别过程的影响。旨在提高表情识别的结果,最终实现高鲁棒性的三维表情识别。     

Simultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion

The recognition of facial gestures and expressions in image sequences is an important and challenging problem. Most of the existing methods adopt the following paradigm. First, facial actions/features are retrieved from the images, then the facial expression is recognized based on the retrieved temporal parameters. In contrast to this mainstream approach, this paper introduces a new approach allowing the simultaneous retrieval of facial actions and expression using a particle filter adopting multi-class dynamics that are conditioned on the expression. For each frame in the video sequence, our approach is split into two consecutive stages. In the first stage, the 3D head pose is retrieved using a deterministic registration technique based on Online Appearance Models. In the second stage, the facial actions as well as the facial expression are simultaneously retrieved using a stochastic framework based on second-order Markov chains. The proposed fast scheme is either as robust as, or more robust than existing ones in a number of respects. We describe extensive experiments and provide evaluations of performance to show the feasibility and robustness of the proposed approach.     

深度人脸表情识别研究进展

随着人脸表情识别任务逐渐从实验室受控环境转移至具有挑战性的真实世界环境,在深度学习技术的迅猛发展下,深度神经网络能够学习出具有判别能力的特征,逐渐应用于自动人脸表情识别任务。目前的深度人脸表情识别系统致力于解决以下两个问题：1）由于缺乏足量训练数据导致的过拟合问题;2）真实世界环境下其他与表情无关因素变量（例如光照、头部姿态和身份特征）带来的干扰问题。本文首先对近十年深度人脸表情识别方法的研究现状以及相关人脸表情数据库的发展进行概括。然后,将目前基于深度学习的人脸表情识别方法分为两类：静态人脸表情识别和动态人脸表情识别,并对这两类方法分别进行介绍和综述。针对目前领域内先进的深度表情识别算法,对其在常见表情数据库上的性能进行了对比并详细分析了各类算法的优缺点。最后本文对该领域的未来研究方向和机遇挑战进行了总结和展望：考虑到表情本质上是面部肌肉运动的动态活动,基于动态序列的深度表情识别网络往往能够取得比静态表情识别网络更好的识别效果。此外,结合其他表情模型如面部动作单元模型以及其他多媒体模态,如音频模态和人体生理信息能够将表情识别拓展到更具有实际应用价值的场景。     

BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

Facial expression is central to human experience. Its efficiency and valid measurement are challenges that automated facial image analysis seeks to address. Most publically available databases are limited to 2D static images or video of posed facial behavior. Because posed and un-posed (aka “spontaneous”) facial expressions differ along several dimensions including complexity and timing, well-annotated video of un-posed facial behavior is needed. Moreover, because the face is a three-dimensional deformable object, 2D video may be insufficient, and therefore 3D video archives are required. We present a newly developed 3D video database of spontaneous facial expressions in a diverse group of young adults. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains. To the best of our knowledge, this new database is the first of its kind for the public. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action.     

Real-time recovery of moving 3D faces for emerging applications

Existing face imaging systems are not suitable to meet the face representation and recognition demands for emerging applications in areas such as interactive gaming, enhanced learning environments and directed advertising. This is mainly due to the poor capture and characterisation of facial data that compromises their spatial and temporal precision. For emerging applications it is not only necessary to have a high level of precision for the representation of facial data, but also to characterise dynamic faces as naturally as possible and in a timely manner. This study proposes a new framework for capturing and recovering dynamic facial information in real-time at significantly high order of spatial and temporal accuracy to capture and model subtle facial changes for enhanced realism in 3D face visualisation and higher precision for face recognition applications. We also present a novel, fast, and robust correspondence mapping approach for 3D registration of moving 3D faces.     

Real-time 2D+3D facial action and expression recognition

This paper presents a completely automated facial action and facial expression recognition system using 2D+3D images recorded in real-time by a structured light sensor. It is based on local feature tracking and rule-based classification of geometric, appearance and surface curvature measurements. Several experiments conducted under relatively non-controlled conditions demonstrate the accuracy and robustness of the approach.     

Hi4D-ADSIP 3-D dynamic facial articulation database

The face is an important medium used by humans to communicate, and facial articulation also reflects a person's emotional and awareness states, cognitive activity, personality or wellbeing. With the advances in 3-D imaging technology and ever increasing computing power, automatic analysis of facial articulation using 3-D sequences is becoming viable. This paper describes Hi4D-ADSIP — a comprehensive 3-D dynamic facial articulation database, containing scans with high spatial and temporal resolution. The database is designed not only to facilitate studies on facial expression analysis, but also to aid research into clinical diagnosis of facial dysfunctions. The database currently contains 3360 facial sequences captured from 80 healthy volunteers (control subjects) of various age, gender and ethnicity. The database has been validated using psychophysical experiments used to formally evaluate the accuracy of the recorded expressions. The results of baseline automatic facial expression recognition methods using Eigen- and Fisher-faces are also presented alongside some initial results obtained for clinical cases. This database is believed to be one of the most comprehensive repositories of facial 3-D dynamic articulations to date. The extension of this database is currently under construction aiming at building a comprehensive repository of representative facial dysfunctions exhibited by patients with stroke, Bell's palsy and Parkinson's disease.