基于SVM的面部表情分析

以面部表情视频序列为研究对象,基于一种统计学习方法-支持向量机对面部表情进行识别及强度度量。采用一种改进的特征点跟踪方法来提取面部特征形变。通过非线性降维方法-等容特征映射自动产生表情强度范围,从高维特征点轨迹中抽取一维的表情强度。最后,使用SVM建立表情模型和强度模型,进行表情的分类,并对高兴表情进行强度等级的归类。实验证明了该表情分析方法的有效性。     

基于PCA与SVM结合的面部表情识别的智能轮椅控制

为了实现基于面部表情识别的智能轮椅控制,在传统的支持向量机(SVM)面部表情识别与分类方法的基础上,采用基于"八眼"的面部有效区域提取方法,将基于主成分分析(PCA)的面部表情特征提取方法与支持向量机分类方法相结合,实现了面部表情的识别与分类,并最终实现基于面部表情识别的智能轮椅的运动控制。实验结果表明,所采用的方法在识别率上明显优于传统SVM与PCA方法。     

基于局部SVM分类器的表情识别方法

提出了一种新的视频人脸表情识别方法. 该方法将识别过程分成人脸表情特征提取和分类2个部分,首先采用基于点跟踪的活动形状模型(ASM)从视频人脸中提取人脸表情几何特征;然后,采用一种新的局部支撑向量机分类器对表情进行分类. 在Cohn2Kanade数据库上对KNN、SVM、KNN2SVM和LSVM 4种分类器的比较实验结果验证了所提出方法的有效性.     

基于混合方法的人脸表情识别研究

针对人脸表情图像识别进行了研究,为了提高表情图像的识别率,提出了一种综合Gabor小波、离散余弦变换、支持向量机的人脸面部表情识别方法。采用ISOMAP算法对人脸面部表情的分类以及强度鉴别。利用Gabor小波变换的局部化特点滤掉人脸表情图像中的高频信息,提出结合离散余弦变换提取系数作为识别特征,并用支持向量机的训练样本构造表情强度模型,进行仿真实验。实验结果表明方法与传统的识别方法相比,系统具有良好的鲁棒性,达到较高的识别率,并能实现了人脸面部表情的分类和强度鉴别。因此充分证明了方法的有效性。     

基于RELS-TSVM的电能表电压异常数据识别

为提高电能计量工作中高压用户电能表电压异常数据识别的快速性和准确性,以负荷控制模块深度分析功能所筛选出的电压数据作为研究对象,结合鲁棒能量模型-最小二乘双支持向量机分类方法,采用蝙蝠算法对分类器参数进行优化,实现对电压异常数据的自动判断。实验结果表明,本文所提出的分类模型在泛化性能和鲁棒性方面具有较大改进,在10折交叉验证过程中对于100V、57.7V、220V额定电压等级下的电压异常数据平均识别准确率均达到100%,相比于传统的经验公式判别、决策树分类模型以及简单支持向量机分类方法在分类准确率上分别提升12.53%、3.8%、1.73%。验证了基于鲁棒能量模型-最小二乘双支持向量机分类方法的电压异常数据识别方案的可行性和优势,为基于大数据分析的计量在线监测相关研究提供了新的思路。     

基于局部二元模式的面部表情识别研究

提出了一种基于局部二元模式（Local Binary Pattern,LBP）与支持向量机（SVM）相结合的面部表情识别方法。使用LBP算子对图像进行处理,对图像的模式进行统计形成面部表情特征;使用线性判别分析对表情特征进行降维处理;采用支持向量机对面部表情进行分类。用Matlab实现了上述方法,并在日本女性人脸表情（JAFFE）数据库上测试,取得了70.95%的识别率。     

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.     

基于三维不变矩的人脸表情识别系统研究

本文提出了基于三维不变矩的人脸表情识别系统研究框架。在对三维人脸表情模型进行预处理及归一化的基础上建立具有平移比例和旋转不变性的三维人脸表情模型的矩不变量特征表示。针对人脸表情分类应用背景采用基于支持向量机的智能学习技术研究实现利用多维度3D-Zernike描述子特征的人脸分析识别系统的建模,基于D-ABC算法的分析识别模型优化,提高模型可靠性和系统的鲁棒性,进而利用该系统进行三维人脸表情数据的分类识别。     

基于加权多头并行注意力的局部遮挡面部表情识别

面部表情识别在诸多领域具有广泛的应用价值, 但在识别过程中局部遮挡会导致面部难以提取有效的表情识别特征, 而局部遮挡的面部表情识别可能需要多个区域的表情特征, 单一的注意力机制无法同时关注面部多个区域特征. 针对这一问题, 本文提出了一种基于加权多头并行注意力的局部遮挡面部表情识别模型, 该模型通过并行多个通道-空间注意力提取局部未被遮挡的多个面部区域表情特征, 有效缓解了遮挡对表情识别的干扰, 大量的实验结果表明, 本文的方法相比于很多先进的方法取得了最优的性能, 在RAF-DB和FERPlus上的准确率分别为89.54%、89.13%, 在真实遮挡的数据集Occlusion-RAF-DB和Occlusion-FERPlus的准确率分别为87.47%、86.28%. 因此, 本文的方法具有很强的鲁棒性.     

基于SLPP与MKSVM的痛苦表情识别

为提高痛苦表情识别的准确率,提出一种基于监督保局投影（SLPP）与多核线性混合支持向量机（MKLMSVM）的识别方法。引入先验类标签信息的SLPP获取痛苦表情特征,以解决保局投影方法在未使用先验类标签信息的情况下忽略类内局部结构的问题,并采用MKLMSVM实现痛苦表情的分类。实验结果表明,该方法的识别准确率可达88．56％,明显优于主动外观模型方法,与一般的支持向量机分类相比,可以提升决策函数的可解释性及分类性能。     

基于Gabor、Fisher脸多特征提取及集成SVM的人脸表情识别*

针对于静态的灰度图像表情库,提出了基于多种脸部表情特征多级分类的表情识别算法。首先在选取的人脸特征点上做局部的Gabor小波变换,为了提高特征提取速度,利用改进的弹性图匹配算法来提取图像中的人脸有效区域,在提取的人脸区域中提取几何特征,并通过Fisher脸法提取统计特征,利用几何特征与建立的相应一级集成SVM来进行初次分类,最后利用Fisher特征与建立的相应二级集成SVM进行最终分类。通过在JAFFE与Cohn-Kanade表情库中实验,证明本文方法同单个特征相比较,具有更高的表情识别率以及更强的鲁棒性     

A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.     

基于改进的非负矩阵分解的人脸表情识别

针对存在部分遮挡的人脸,提出了一种基于改进的非负矩阵分解的人脸表情识别方法,首先,用改进的非负矩阵分解算法对人脸图像进行表情特征提取,然后用最大相关分类器对面部表情进行分类。在Cohn-Kanade人脸表情数据库上的实验,结果表明,该方法提高了无遮挡的人脸表情识别,对有遮挡的人脸表情识别也有改善。     

Differential optical flow applied to automatic facial expression recognition

This work compares systematically two optical flow-based facial expression recognition methods. The first one is featural and selects a reduced set of highly discriminant facial points while the second one is holistic and uses much more points that are uniformly distributed on the central face region. Both approaches are referred as feature point tracking and holistic face dense flow tracking, respectively. They compute the displacements of different sets of points along the sequence of frames describing each facial expression (i.e. from neutral to apex). First, we evaluate our algorithms on the Cohn-Kanade database for the six prototypic expressions under two different spatial frame resolutions (original and 40%-reduced). Later, our methods were also tested on the MMI database which presents higher variabilities than the Cohn-Kanade one. The results on the first database show that dense flow tracking method at original resolution slightly outperformed, in average, the recognition rates of feature point tracking method (95.45% against 92.42%) but it requires 68.24% more time to track the points. For the patterns of MMI database, using dense flow tracking at the original resolution, we achieved very similar average success rates.     

基于可分度和支持度的模糊密度赋值融合识别算法

模糊积分理论可有效处理分类决策不确定性问题。当前模糊密度的确定方法未考虑各个分类器识别结果的可区分程度及各分类器对识别结果的支持程度,会丢失融合识别的相关信息。文中提出基于可分度和支持度的自适应模糊密度赋值融合识别算法。该算法根据各分类器对待识别样本的识别结果的可区分程度和支持程度对分类器的融合模糊密度进行自适应赋值,从而有效实现多分类器融合识别。将该算法应用于自然交互环境下的人脸表情识别和Cohn-Kanade表情识别。实验结果表明,该算法能有效提高总体表情识别率。     

Recognition of facial expressions and measurement of levels of interest from video

This paper presents a spatio-temporal approach in recognizing six universal facial expressions from visual data and using them to compute levels of interest. The classification approach relies on a two-step strategy on the top of projected facial motion vectors obtained from video sequences of facial expressions. First a linear classification bank was applied on projected optical flow vectors and decisions made by the linear classifiers were coalesced to produce a characteristic signature for each universal facial expression. The signatures thus computed from the training data set were used to train discrete hidden Markov models (HMMs) to learn the underlying model for each facial expression. The performances of the proposed facial expressions recognition were computed using five fold cross-validation on Cohn-Kanade facial expressions database consisting of 488 video sequences that includes 97 subjects. The proposed approach achieved an average recognition rate of 90.9% on Cohn-Kanade facial expressions database. Recognized facial expressions were mapped to levels of interest using the affect space and the intensity of motion around apex frame. Computed level of interest was subjectively analyzed and was found to be consistent with "ground truth" information in most of the cases. To further illustrate the efficacy of the proposed approach, and also to better understand the effects of a number of factors that are detrimental to the facial expression recognition, a number of experiments were conducted. The first empirical analysis was conducted on a database consisting of 108 facial expressions collected from TV broadcasts and labeled by human coders for subsequent analysis. The second experiment (emotion elicitation) was conducted on facial expressions obtained from 21 subjects by showing the subjects six different movies clips chosen in a manner to arouse spontaneous emotional reactions that would produce natural facial expressions.     

基于子空间稀疏系数的表情识别方法

提出了一种运用SRC（Sparse Representation based Classification）在个体子空间里,进行表情识别的新方法。用Gabor滤波器,提取表情图像的特征。进行稀疏分解,得到稀疏表示系数。根据稀疏系数确定待测图像所在的子空间,在子空间里,完成表情识别。这种方法较好地避免了不同个体对表情识别的干扰,从而提高了表情识别的正确率。在Cohn-Kanade和JAFFE人脸库上的表情识别实验表明,该方法对表情识别非常有效。     

多尺度中心误差补偿二值模式的表情识别

针对Gabor小波与局部二值模式（Local Binary Pattern,LBP）在表情识别上的局限性,提出了一种多尺度中心误差补偿二值模式（Center Error Compensation Binary Pattern,CECBP）的表情识别方法。对预处理后的人脸表情图像创建多尺度的金字塔,用中心误差补偿二值模式对金字塔中的各层图像进行编码,分块提取各层编码后的直方图序列作为特征,用支持向量机（Support Vector Machine,SVM）进行分类。在JAFFE、Cohn-Kanade以及Pain Expression表情库上的交叉验证表明,该方法可以抑制噪声,具有较高的识别率和较快的识别速度,比传统的Gabor小波以及LBP更具有优势。     

Saliency-based framework for facial expression recognition

This article proposes a novel framework for the recognition of six universal facial expressions. The framework is based on three sets of features extracted from a face image: entropy, brightness, and local binary pattern. First, saliency maps are obtained using the state-of-the-art saliency detection algorithm “frequency-tuned salient region detection”. The idea is to use saliency maps to determine appropriate weights or values for the extracted features (i.e., brightness and entropy).We have performed a visual experiment to validate the performance of the saliency detection algorithm against the human visual system. Eye movements of 15 subjects were recorded using an eye-tracker in free-viewing conditions while they watched a collection of 54 videos selected from the Cohn-Kanade facial expression database. The results of the visual experiment demonstrated that the obtained saliency maps are consistent with the data on human fixations. Finally, the performance of the proposed framework is demonstrated via satisfactory classification results achieved with the Cohn-Kanade database, FG-NET FEED database, and Dartmouth database of children’s faces.     

基于特征融合与决策树技术的表情识别方法

针对复杂状况下传统表情识别方法存在的问题,提出一种新的非特定人表情识别方法。该算法首先提取每张表情图像的HOG特征和Haar小波特征,然后将两种不同的特征串行融合得到整幅图像的特征,最后通过SVM多分类器完成各层人脸表情的分类识别。在JAFFE人脸表情库上的仿真实验中,该方法的分类准确率达到87.9%,平均时耗达到10.296 6s。对比实验结果表明,所提算法具有更高的识别率、更好的实时性和更强的鲁棒性。