有监督低秩子空间恢复的正则鲁棒稀疏表示人脸识别算法

针对训练样本和测试样本均存在光照及遮挡时,破坏图像低秩结构问题,本文提出基于监督低秩子空间恢复的正则鲁棒稀疏表示人脸识别算法。首先,将所有训练样本构造成矩阵D,对矩阵D进行监督低秩矩阵分解,分解为低秩类相关结构A,低秩类内差异结构B和稀疏误差结构E;然后用主成分分析方法找到类相关结构A低秩子空间的变换矩阵;再通过变换矩阵将训练样本和测试样本投影到低秩子空间;最后,在低秩子空间中,通过正则鲁棒稀疏编码进行加权分类识别。在AR和Extended Yale B公开人脸数据库上的实验结果验证本文算法的有效性及鲁棒性。      

Multimodal emotion recognition based on peak frame selection from video

We present a fully automatic multimodal emotion recognition system based on three novel peak frame selection approaches using the video channel. Selection of peak frames (i.e., apex frames) is an important preprocessing step for facial expression recognition as they contain the most relevant information for classification. Two of the three proposed peak frame selection methods (i.e., MAXDIST and DEND-CLUSTER) do not employ any training or prior learning. The third method proposed for peak frame selection (i.e., EIFS) is based on measuring the “distance” of the expressive face from the subspace of neutral facial expression, which requires a prior learning step to model the subspace of neutral face shapes. The audio and video modalities are fused at the decision level. The subject-independent audio-visual emotion recognition system has shown promising results on two databases in two different languages (eNTERFACE and BAUM-1a).     

Utility Preserved Facial Image De-identification Using Appearance Subspace Decomposition

Automated human facial image de-identification is a much-needed technology for privacy-preserving social media and intelligent surveillance ap-plications. We propose a novel utility preserved facial image de-identification to subtly tinker the appearance of facial images to achieve facial anonymity by creating"averaged identity faces". This approach is able to preserve the utility of the facial images while achieving the goal of privacy protection. We explore a decomposition of an Active appearance model (AAM) face space by using subspace learning where the loss can be modeled as the difference between two trace ratio items, and each respectively models the level of discriminativeness on identity and utility. Finally, the face space is decomposed into subspaces that are respectively sensitive to face identity and face utility. For the subspace most relevant to face identity, a k-anonymity de-identification procedure is applied. To verify the performance of the proposed facial image de-identification approach, we evaluate the created"averaged faces"using the extended Cohn-Kanade Dataset (CK+). The experimental results show that our proposed approach is satisfied to preserve the utility of the original image while defying face identity recognition.     

人脸显性特征的融合构造方法及识别

 目前的人脸识别研究中,面部几何特征没有得到很好的利用.本文阐述了几何特征对于人脸识别的重要性,在此基础上提出了一种提取面部几何特征的新方法;通过融合几何信息和纹理信息构造出一种面部显性特征,并给出了相应的人脸识别方法.这种新的人脸识别方法相对于基于统计学习的子空间方法具有一定的优势,同时也可作为后者的有益补充.实验表明,本文提出的人脸表示特征及识别方法对人脸表情变化和环境光照变化均有一定的鲁棒性.     

一种基于分块个体子空间的人脸识别方法

针对传统PCA算法的局限性,本文提出了一种基于个体子空间和分块特征提取的人脸识别方法。该方法不同于传统的PCA算法,它为每一个体人脸对象建立一个该个体对象所独有的人脸子空间,并且运用分块特征提取,加强了对样本局部特征信息的提取。在识别过程中,将待测人脸图像对不同个体人脸子空间进行投影后,再运用最近距离分类原则进行分类识别。该方法相对于传统的PCA算法能够更好地描述不同个体人脸之间的差异性,克服各类噪声。本文的实验结果表明,该方法的识别率相对于传统PCA特征脸算法具有一定的提高,能够较好地克服光照、表情和姿态变化的影响。     

基于图像模糊度与主成分分析的车牌汉字识别

针对车牌汉字字符结构复杂且图像品质差异大而导致识别率不高的情况,提出了一种基于图像模糊度的主成分分析(PCA)子空间车牌汉字字符识别方法。首先通过三角模和非模糊基数计算字符图像的模糊度,然后根据模糊度将训练样本分成不同的子集并生成相应的PCA子空间族,最后以待识别字符的模糊度为依据选择相应的子空间族进行识别。实验数据表明,本文方法使得子类的类内距离变小类间距离增大,从而可以获得较高的识别率。与其他算法的对比实验进一步表明,本文算法能更好地同时满足精度和实时性的要求,具有良好的综合性能。     

Human emotion recognition by optimally fusing facial expression and speech feature

Emotion recognition is a hot research in modern intelligent systems. The technique is pervasively used in autonomous vehicles, remote medical service, and human–computer interaction (HCI). Traditional speech emotion recognition algorithms cannot be effectively generalized since both training and testing data are from the same domain, which have the same data distribution. In practice, however, speech data is acquired from different devices and recording environments. Thus, the data may differ significantly in terms of language, emotional types and tags. To solve such problem, in this work, we propose a bimodal fusion algorithm to realize speech emotion recognition, where both facial expression and speech information are optimally fused. We first combine the CNN and RNN to achieve facial emotion recognition. Subsequently, we leverage the MFCC to convert speech signal to images. Therefore, we can leverage the LSTM and CNN to recognize speech emotion. Finally, we utilize the weighted decision fusion method to fuse facial expression and speech signal to achieve speech emotion recognition. Comprehensive experimental results have demonstrated that, compared with the uni-modal emotion recognition, bimodal features-based emotion recognition achieves a better performance.     

改进非负矩阵分解的神经网络人脸识别

为了提高人脸识别正确率,提出基于改进非负矩阵分解的神经网络人脸识别算法。首先利用改进的非负矩阵分解对人脸图像进行特征提取,提高非负矩阵分解速度。接着将提取出的特征信息作为神经网络学习入口进行特征训练,由于神经网络在学习过程中,容易出现局部最小值且收敛速度慢等问题,为此采用改进的遗传算法对神经网络进行优化处理,获得最终的人脸识别结果。实验结果表明:利用改进的非负矩阵分解方法能够降低神经网络的分类训练负荷量和运算量,提高人脸识别识别率。通过和各种方法比较可知,本方法的人脸识别率都较高。本方法人脸特征分解速度快,提高了神经网络训练前期精度和收敛速度,使得人脸识别正确率高。当特征向量个数达到40以上时,人脸识别正确率保持95%以上。     

基于加权平均脸的最近邻算法

提出了一种基于加权平均脸的最近邻算法。该方法提取与测试样本距离较近的几个样本点,并用提取出的样本点组成加权平均脸。加权平均脸方法扩展了人脸图像的姿态和表情变化,在ORL人脸库和YALE人脸库上的实验表明,该方法提高了人脸样本的识别率：     

基于多特征融合的道路交通标志检测

在道路交通标志的检测中,针对自然实景情况中拍摄到的图像存在的交通标志大小和位置不确定等困难问题,本文提出一种基于实景图像的多特征融合的道路交通标志检测方法。论文把样本分为了训练和测试样本,首先对训练样本图像进行盲复原处理;其次对复原处理后的图像进行自适应性的形状区域裁剪,提取裁剪区域图像的颜色、纹理和形状特征;再次分别对颜色、纹理和形状特征进行SVM分类检测,从而获得颜色、纹理和形状三个分类模型;最后对模型的权值进行自适应性计算,得到加权的特征融合模型。通过测试样本对模型的检测,结果表明特征融合识别方法有很高的准确度,另外对比实验得到的对比数据显示融合模型提高了道路交通检测的准确度和鲁棒性。      

Single sample per person face recognition with KPCANet and a weighted voting scheme

Most current methods of facial recognition rely on the condition of having multiple samples per person available for feature extraction. In practical applications, however, only one sample may be available for each person to train a model with. As a result, many of the traditional methods fall short, leaving the challenge of facial recognition greater than ever. To deal with this challenge, this study addresses a face recognition algorithm based on a kernel principal component analysis network (KPCANet) and then proposes a weighted voting method. First, the aligned face image is partitioned into several non-overlapping patches to form the training set. Next, a KPCANet is used to obtain filters and feature banks. Finally, the identification of the unlabeled probe occurs through the application of the weighted voting method. Based on several widely used face datasets, the results of the experiments demonstrate the superiority of the proposed method.     

基于最大似然可变子空间的快速说话人自适应方法

该文提出一种基于最大似然可变子空间的说话人自适应方法。在训练阶段,对训练集中的说话人相关模型参数进行主分量分析,得到一组说话人基矢量;在自适应阶段,通过最大似然准则选取与当前说话人相关性最大的基矢量子集,进而将新的说话人相关模型限制在这组基矢量所张成的说话人子空间中,通过求解每一个基矢量对应的系数从而进行说话人自适应。与经典的基于子空间的说话人自适应方法不同,该文中的说话人子空间是在自适应阶段动态选取的,所需要估计的参数更少,在少量自适应数据下可以得到更稳健的自适应结果。在基于微软语料库的连续语音识别自适应实验中,给定极少量自适应数据(小于5 s),在有监督和无监督条件下,该文方法均优于经典的本征音自适应方法和基于最大似然线性回归的方法。     

融合局部语义与全局信息的人脸表情识别

人脸表情识别在人机交互等人工智能领域发挥着 重要作用,当前研究忽略了人脸的语 义信息。本 文提出了一种融合局部语义与全局信息的人脸表情识别网络,由两个分支组成:局部语义区 域提取分支 和局部-全局特征融合分支。首先利用人脸解析数据集训练语义分割网络得到人脸语义解析 ,通过迁移训 练的方法得到人脸表情数据集的语义解析。在语义解析中获取对表情识别有意义的区域及其 语义特征, 并将局部语义特征与全局特征融合,构造语义局部特征。最后,融合语义局部特征与全局特 征构成人脸 表情的全局语义复合特征,并通过分类器分为7种基础表情之一。本文同时提出了解冻部分 层训练策略, 该训练策略使语义特征更适用于表情识别,减 少语义信息冗余性。在两个公开数据集JAFFE 和KDEF上 的平均识别准确率分别达到了93.81%和88.78% ,表现优于目前的深度学习方法和传统方法。实验结果证 明了本文提出的融合局部语义和全局信息的网络能够很好地描述表情信息。     

Unsupervised,Supervised and Semi-supervised Dimensionality Reduction by Low-Rank Regression Analysis

Techniques for dimensionality reduction have attracted much attention in computer vision and pattern recognition.However,for the supervised or unsupervised case,the methods combining regression analysis and spectral graph analysis do not consider the global structure of the subspace;For semi-supervised case,how to use the unlabeled samples more effectively is still an open problem.In this paper,we propose the methods by Low-rank regression analysis (LRRA) to deal with these problems.For supervised or unsupervised dimensionality reduction,combining spectral graph analysis and LRRA can make a global constraint on the subspace.For semi-supervised dimensionality reduction,the proposed method incorporating LRRA can exploit the unlabeled samples more effectively.The experimental results show the effectiveness of our methods.     

Decision Fusion With Confidence-Based Weight Assignment for Hyperspectral Target Recognition

Conventional hyperspectral image-based automatic target recognition (ATR) systems project high-dimensional reflectance signatures onto a lower dimensional subspace using techniques such as principal components analysis (PCA), Fisher's linear discriminant analysis (LDA), and stepwise LDA. Typically, these feature space projections are suboptimal. In a typical hyperspectral ATR setup, the number of training signatures (ground truth) is often less than the dimensionality of the signatures. Standard dimensionality reduction tools such as LDA and PCA cannot be applied in such situations. In this paper, we present a divide-and-conquer approach that addresses this problem for robust ATR. We partition the hyperspectral space into contiguous subspaces based on the optimization of a performance metric. We then make local classification decisions in every subspace using a multiclassifier system and employ a decision fusion system for making the final decision on the class label. In this work, we propose a metric that incorporates higher order statistical information for accurate partitioning of the hyperspectral space. We also propose an adaptive weight assignment method in the decision fusion process based on the strengths (as measured by the training accuracies) of individual classifiers that made the local decisions. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies. The proposed system was found to significantly outperform conventional approaches. For example, under moderate pixel mixing, the proposed approach resulted in classification accuracies around 90%, where traditional feature fusion resulted in accuracies around 65%.     

Image classification using correlation tensor analysis

Images, as high-dimensional data, usually embody large variabilities. To classify images for versatile applications, an effective algorithm is necessarily designed by systematically considering the data structure, similarity metric, discriminant subspace, and classifier. In this paper, we provide evidence that, besides the Fisher criterion, graph embedding, and tensorization used in many existing methods, the correlation-based similarity metric embodied in supervised multilinear discriminant subspace learning can additionally improve the classification performance. In particular, a novel discriminant subspace learning algorithm, called correlation tensor analysis (CTA), is designed to incorporate both graph-embedded correlational mapping and discriminant analysis in a Fisher type of learning manner. The correlation metric can estimate intrinsic angles and distances for the locally isometric embedding, which can deal with the case when Euclidean metric is incapable of capturing the intrinsic similarities between data points. CTA learns multiple interrelated subspaces to obtain a low-dimensional data representation reflecting both class label information and intrinsic geometric structure of the data distribution. Extensive comparisons with most popular subspace learning methods on face recognition evaluation demonstrate the effectiveness and superiority of CTA. Parameter analysis also reveals its robustness.     

Discriminant learning through multiple principal angles for visual recognition

Canonical correlation has been prevalent for multiset-based pairwise subspace analysis. As an extension, discriminant canonical correlations (DCCs) have been developed for classification purpose by learning a global subspace based on Fisher discriminant modeling of pairwise subspaces. However, the discriminative power of DCCs is not optimal as it only measures the "local" canonical correlations within subspace pairs, which lacks the "global" measurement among all the subspaces. In this paper, we propose a multiset discriminant canonical correlation method, i.e., multiple principal angle (MPA). It jointly considers both "local" and "global" canonical correlations by iteratively learning multiple subspaces (one for each set) as well as a global discriminative subspace, on which the angle among multiple subspaces of the same class is minimized while that of different classes is maximized. The proposed computational solution is guaranteed to be convergent with much faster converging speed than DCC. Extensive experiments on pattern recognition applications demonstrate the superior performance of MPA compared to existing subspace learning methods.     

Linear subspaces for facial expression recognition

This paper presents a method for the recognition of the six basic facial expressions in images or in image sequences using landmark points. The proposed technique relies on the observation that the vectors formed by the landmark point coordinates belong to a different manifold for each of the expressions. In addition experimental measurements validate the hypothesis that each of these manifolds can be decomposed to a small number of linear subspaces of very low dimension. This yields a parameterization of the manifolds that allows for computing the distance of a feature vector from each subspace and consequently from each one of the six manifolds. Two alternative classifiers are next proposed that use the corresponding distances as input: the first one is based on the minimum distance from the manifolds, while the second one uses SVMs that are trained with the vector of all distances from each subspace. The proposed technique is tested for two scenarios, the subject-independent and the subject-dependent one. Extensive experiments for each scenario have been performed on two publicly available datasets yielding very satisfactory expression recognition accuracy.     

Image enhancement using divide-and-conquer strategy

Existing enhancement methods tend to overlook the difference between image components of low-frequency and high-frequency. However, image low-frequency portions contain smooth areas occupied the majority of the image, while high-frequency components are sparser in the image. Meanwhile, the different importance of image low-frequency and high-frequency components cannot be precisely and effectively for image enhancement. Therefore, it is reasonable to deal with these components separately when designing enhancement algorithms with image subspaces. In this paper, we propose a novel divide-and-conquer strategy to decompose the observed image into four subspaces and enhance the images corresponding to each subspace individually. We employ the existing technique of gradient distribution specification for these enhancements, which has displayed promising results for image naturalization. We then reconstruct the full image using the weighted fusion of these four subspace images. Experimental results demonstrate the effectiveness of the proposed strategy in both image naturalization and details promotion.     

基于人脸相似度加权距离的非特定人表情识别

该文提出了一种用于非特定人表情识别的方法。首先,对测试人的初始表情特征进行高阶奇异值分解,得到测试人与训练集中所有人相关的表情特征。然后,根据相似的人有相似的表情的假设,计算人脸相似度加权距离,作为测试人的表情特征与标准的表情特征之间的相似性测度。通过加权的过程,可以有效地去除由于个体差异而造成的表情特征的差异,提高非特定人表情识别的鲁棒性。该文提出的方法在JAFFE数据库上进行了测试。对非特定人的表情识别实验表明,该文方法比传统的方法在识别率上有了提高。