人脸表情运动跟踪与表情识别算法

对于人脸视频中的每一帧,提出一种静态人脸表情识别算法,人脸表情运动参数被提取出来后,根据表情生理知识来分类表情;为了应对知识的不足,提出一种静态表情识别和动态表情识别相结合的算法,以基于多类表情马尔可夫链和粒子滤波的统计框架结合生理知识来同时提取人脸表情运动和识别表情.实验证明了算法的有效性.     

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.     

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.     

Hierarchical On-line Appearance-Based Tracking for 3D head pose,eyebrows, lips,eyelids and irises

In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can also be used for eyelid and iris tracking, as well as 3D head pose, lips and eyebrows facial actions tracking. Furthermore, our approach applies an on-line learning of changes in the appearance of the tracked target. Hence, the prior training of appearance models, which usually requires a large amount of labeled facial images, is avoided. Moreover, the proposed method is built upon a hierarchical combination of three OABTs, which are optimized using a Levenberg–Marquardt Algorithm (LMA) enhanced with line-search procedures. This, in turn, makes the proposed method robust to changes in lighting conditions, occlusions and translucent textures, as evidenced by our experiments. Finally, the proposed method achieves head and facial actions tracking in real-time.     

基于性别和年龄因子分析的鲁棒性人脸表情识别

针对非可控环境下人脸表情识别面临的诸如种族、性别和年龄等因子变化问题,提出一种基于深度条件随机森林的鲁棒性人脸表情识别方法.与传统的单任务人脸表情识别方法不同,设计了一种以人脸表情识别为主,人脸性别和年龄属性识别为辅的多任务识别模型.在研究中发现,人脸性别和年龄等属性对人脸表情识别有一定的影响,为了捕获它们之间的关系,提出一种基于人脸性别和年龄双属性的深度条件随机森林人脸表情识别方法.在特征提取阶段,采用多示例注意力机制进行人脸特征提取以便去除诸如光照、遮挡和低分辨率等变化问题;在人脸表情识别阶段,根据人脸性别和年龄双属性因子,采用多条件随机森林方法进行人脸表情识别.在公开的CK+,ExpW,RAF-DB,AffectNet人脸表情数据库上进行了大量实验:在经典的CK+人脸库上达到99%识别率,在具有挑战性的自然场景库(ExpW,RAF-DB,AffectNet组合库)上达到70.52%的识别率.实验结果表明:与其他方法相比具有先进性,对自然场景中的遮挡、噪声和分辨率变化具有一定的鲁棒性.     

A Video-Based Facial Motion Tracking and Expression Recognition System

We proposed a facial motion tracking and expression recognition system based on video data. By a 3D deformable facial model, the online statistical model (OSM) and cylinder head model (CHM) were combined to track 3D facial motion in the framework of particle filtering. For facial expression recognition, a fast and efficient algorithm and a robust and precise algorithm were developed. With the first, facial animation and facial expression were retrieved sequentially. After that facial animation was obtained, facial expression was recognized by static facial expression knowledge learned from anatomical analysis. With the second, facial animation and facial expression were simultaneously retrieved to increase the reliability and robustness with noisy input data. Facial expression was recognized by fusing static and dynamic facial expression knowledge, the latter of which was learned by training a multi-class expressional Markov process using a video database. The experiments showed that facial motion tracking by OSM+CHM is more pose robust than that by OSM, and the facial expression score of the robust and precise algorithm is higher than those of other state-of-the-art facial expression recognition methods.     

An Efficient Adaptive Network-Based Fuzzy Inference System with Mosquito Host-Seeking For Facial Expression Recognition

In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB.     

基于侧面轮廓线和刚性区域的3维人脸识别

针对3维人脸识别问题,提出一种由粗到细的两步识别方法。首先结合几何约束与曲率信息定位特征点,根据特征点确定人脸对称面,提取人脸侧面轮廓线。利用轮廓线匹配作为排除算法,在识别初期迅速排除库集中不相似人脸以提高识别效率,剩余库集人脸采用一种具有表情鲁棒性的、基于区域的匹配方法进行识别,该方法自动切割人脸中受表情影响较小的刚性区域,并采用改进的迭代最近点算法对刚性区域进行匹配,为达到更好的识别精度,将各刚性区域的匹配结果采用加法规则融合。在3D_RMA人脸数据库的实验结果表明,该方法具有较好的实时性和鲁棒性。     

Face recognition with Symmetric Local Graph Structure (SLGS)

Face recognition demonstrates the significant progress in the research field of biometric and computer vision. The fact is due to the current systems perform well under relatively control environments but tend to suffer when the present of variation in pose, illumination, and facial expression. In this work, a novel approach for face recognition called Symmetric Local Graph Structure (SLGS) is presented based on the Local Graph Structure (LGS). Each pixel is represented with a graph structure of its neighbours’ pixels. The histograms of the SLGS were used for recognition by using the nearest neighbour classifiers that include Euclidean distance, correlation coefficient and chi-square distance measures. AT&T and Yale face databases were used to be experimented with the proposed method. Extensive experiments on the face database clearly showed the superiority of the proposed approach over Local Binary Pattern (LBP) and LGS. The proposed SLGS is robust to variation in term of facial expressions, facial details, and illumination. Due to good performance of SLGS, it is expected that SLGS has a potential for application implementation in computer vision.     

融合LBP和局部稀疏表示的人脸表情识别

为更好获取人脸局部表情特征,提出了一种融合局部二值模式（Local Binary Pattern,LBP）和局部稀疏表示的人脸表情特征与识别方法。为深入分析表情对人脸子区域的影响,根据五官特征对人脸进行非均匀分区,并提取局部LBP特征;为精细刻画人脸局部纹理,整合人脸局部特征,设计了人脸局部稀疏重构表示方法,并根据表情对各局部子区域的影响因子,加权融合局部重构残差进行人脸表情识别。在JAFFE2表情人脸库上的对比实验,验证了该方法的可行性和鲁棒性。     

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。     

A graphical model based solution to the facial feature point tracking problem

In this paper a facial feature point tracker that is motivated by applications such as human-computer interfaces and facial expression analysis systems is proposed. The proposed tracker is based on a graphical model framework. The facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated on real video data under various conditions including occluded facial gestures and head movements. It is also compared to two popular methods, one based on Kalman filtering exploiting temporal relations, and the other based on active appearance models (AAM). Improvements provided by the proposed approach are demonstrated through both visual displays and quantitative analysis.     

基于表情相似性的人脸表情流形

在图嵌入(graph embedding)的框架下提出一种根据表情相似度构建邻接权重图的方法来学习人脸表情子空间.数据集中人脸图像的表情以半监督-学习的方式来估计,人脸图像之间的表情相似性由表情模糊隶属度矢量之间的内积来度量,与个体、光照、姿态等人脸差异无关.在得到的子空间内,相似表情的人脸图像位于流形上的邻近位置,表情数据在子空间内按语义的分布很好地揭示了表情模糊、演变的特性.在Cohn-Kanade人脸表情数据库和实验室自行采集的人脸表情数据集上的实验结果说明了该方法的有效性.因此,该方法可以很好地应用于各种基于人脸表情识别的人机交互中.     

面部表情图像的分析与识别

本文通过对若干类面部表情图像的分析，建立了基于部件分解组合的人脸图像模型。根据对部件形状和相对位置的分析，提出了表情的分类树，建立了表情模型的向量表示。根据能量优化原理，利用模板匹配方法提取目标特征，得到人脸表情的表征向量，由模式分类方法实现表情的识别。     

人脸表情的LBP特征分析

为了有效提取面部表情特征,提出了一种新的基于LBP（局部二值模式）特征的人脸表情识别特征提取方法。首先用均值方差法对表情图像进行灰度规一化,通过对图像进行积分投影,定位出眉毛、眼睛、鼻和嘴巴这些关键特征点,进而划分出各特征部件所在子区域,然后对子区域进行分块,提取各个子区域的分块LBP直方图特征。为了验证所提出的方法的合理性,最后在JAFFE表情库上进行了实验,结果表明提出的方法能够有效地描述表情的特征。     

基于Gabor直方图特征和MVBoost的人脸表情识别

提出采用Gabor变换与分级直方图统计相结合的方法来提取表情特征,以分层次反映局部区域内纹理变化的信息.这比仅用一维的Gabor系数具有更强的特征表示能力.借助直方图特征,还设计了向量输入、多类连续输出的弱分类器,并嵌入到多类连续AdaBoost的算法框架中,得到了向量输入、多类输出的MVBoost方法.该方法直接对特征进行多类的判决以满足多类时分类的需求,而不必训练多个二分类的AdaBoost分类器,从而使训练过程和分类过程都得到简化.     

基于压缩感知的鲁棒性人脸表情识别

为了有效提高噪声背景下的人脸表情识别性能,提出一种基于压缩感知的鲁棒性人脸表情识别方法.先通过对腐蚀的测试样本表情图像进行稀疏表示,再利用压缩感知理论寻求其最稀疏的解,然后采用求得的最稀疏解信息实现人脸表情的分类.在标准的Cohn-Kanade表情数据库的实验测试结果表明,该方法取得的人脸表情识别性能优于最近邻法、支持向量机以及最近邻子空间法.可见,该方法用于人脸表情识别,识别效果较好,鲁棒性较高.     

Illuminant-Dependence of Von Kries Type Quotients

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods.The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.     

基于VGG-NET的特征融合面部表情识别

为了解决在面部表情特征提取过程中卷积神经网络CNN和局部二值模式LBP只能提取面部表情图像的单一特征,难以提取与面部变化高度相关的精确特征的问题,提出了一种基于深度学习的特征融合的表情识别方法。该方法将LBP特征和CNN卷积层提取的特征通过加权的方式结合在改进的VGG-16网络连接层中,最后将融合特征送入Softmax分类器获取各类特征的概率,完成基本的6种表情分类。实验结果表明,所提方法在CK+和JAFFE数据集上的平均识别准确率分别达到了97.5%和97.62%,利用融合特征得到的识别结果明显优于利用单一特征识别的效果。与其他方法相比较,该方法能有效提高表情识别准确率,对光照变化更加鲁棒。     

Feature representation for facial expression recognition based on FACS and LBP

In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and "uniform" local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience.