字典学习优化判别性降维的鲁棒人脸识别

针对现有的人脸识别算法由于光照、表情、姿态、面部遮挡等变化而严重影响识别性能的问题,提出了基于字典学习优化判别性降维的鲁棒人脸识别算法。首先,利用经典的特征提取算法PCA初始化降维投影矩阵;然后,计算字典和系数,通过联合降维与字典学习使得投影矩阵和字典更好地相互拟合;最后,利用迭代算法输出字典和投影矩阵,并利用经l2-范数正则化的分类器完成人脸的识别。在扩展YaleB、AR及一个户外人脸数据库上的实验验证了本文算法的有效性及鲁棒性,实验结果表明,相比几种线性表示算法,本文算法在处理鲁棒人脸识别时取得了更高的识别率。     

人脸面部表情识别的研究进展

随着信息和计算机技术的飞速发展,人脸面部表情识别技术越来越受到重视.本文综述了近年来人脸面部表情识别的研究进展.首先对面部表情识别技术的研究背景和发展历程作了简单的回顾,然后着重介绍了几种主要的识别方法,并力争从理论上对各种方法进行分析和比较,最后简单地讨论了进一步提高面部表情识别率的难点和必须考虑的几个重要方面,进而展望了人脸面部表情识别技术的发展方向.     

通用学习框架结合2DPCA的鲁棒人脸识别

针对现有的人脸识别算法由于光照、表情、姿态、伪装等变化而严重影响识别性能的问题,提出了一种基于通用学习框架结合2DPCA的鲁棒人脸识别算法。首先借助于额外的通用训练样本集进行样本的叠加以增加训练样本的数量;然后利用经典的2DPCA算法进行特征提取;最后,利用最近邻分类器对人脸进行分类并完成最终的人脸识别。在基准人脸数据库ORL、FERET及鲁棒人脸数据库AR、扩展YaleB上的实验验证了该算法的有效性及鲁棒性,实验结果表明,相比其他几种人脸识别算法,提出的算法不仅提高了人脸识别率,而且大大地减少了识别所用时间,有望应用于实时鲁棒人脸自动识别系统中。     

面部表情识别中基于TTL的特定个体学习模型

针对现实生活中采集的特定个体数据稀疏而导致学习时产生过拟合的问题,提出了一种基于迁移学习的特定个体学习模型。首先,利用直推式迁移学习从原数据提取有用信息并将其添加到目标数据;然后,利用核主成分分析进行特征提取;最后,将各个样本建模为流形,并利用稀疏系数重建和k近邻分类器完成识别。在PAINFUL数据库上的实验结果表明,本文模型在痛苦表情上的识别精度可高达96.29%,ROC下方面积可高达0.904,相比其他几种较新的模型,本文模型取得了更好的识别性能。     

基于兴趣区域深度神经网络的静态面部表情识别

通过在面部表情数据集上训练深度卷积神经网络、深度稀疏校正神经网络两种模型,对两种深度神经网络在静态面部表情识别方面的应用作了对比和分析.基于面部表情的结构先验知识,提出一种面向面部表情识别的改良方法——K兴趣区域方法,该方法在构建的开放实验数据集上,降低了由于训练数据过少而导致深度神经网络模型泛化能力不佳的问题,使得混合模型普遍且显著地降低了测试错误率.进而,结合实验结果进行了深入分析,并对深度神经网络在任意图像数据集上的可能有效性进行了深入剖析和分析.     

基于融合DCT和LBP特征提取的面部表情识别

针对离散余弦变换(DCT)只能提取面部表情图像的全局特征,而忽略了临近像素之间的关系、不能提取纹理特征信息、不能准确区分相似表情等问题,提出一种融合离散余弦变换方法和局部二值模式(LBP)特征的表情特征提取方法。该方法首先将人脸图像经过DCT获得的低频系数作为表情的全局特征;然后用LBP对贡献率较大的嘴部、眼睛区域进行局部纹理特征提取,通过将LBP提取到的局部纹理特征与DCT提取到的全局特征进行融合,从而得到更有效的表情特征;最后利用支持向量机(SVM)进行识别。实验结果表明:该方法比单独使用DCT方法提取的表情特征更有利于识别,提高了表情识别的准确性,并将这个表情识别方法用于智能轮椅的控制上,收到了良好的效果。     

基于改进Inception V4的面部表情识别算法的研究

面部表情识别是地铁、火车站、机场等复杂环境中安检监控的重要任务,通过识别监控图像中行人的面部表情可以筛选出可疑分子。针对因监控图像模糊和面部表情拍摄不全而引起的识别准确率低等问题,提出一种改进的InceptionV4面部表情识别算法,改进InceptionV4的网络结构,使其更好地适应面部表情识别任务。基于深度学习中的Tensorflow平台对面部表情类数据进行训练,在面部表情验证集上进行测试,在输入图像为299×299时,识别准确率高达97.9%,改进后的算法在保证识别精度的同时,降低表情在类内差距较大、图像模糊和面部表情拍摄不全情况下的误识率,提高系统鲁棒性。     

基于GAN网络的面部表情识别

本文为了解决姿态不变的面部表情识别即在任意姿态下的面部表情识别问题,提出了一种端到端的深度学习模型,该模型利用不同的姿态和表情进行面部图像合成扩充训练集,提高了模型的准确度,并有效地解决了姿态不变的面部表情识别问题。本文将介绍表情识别的主要过程以及模型中使用到的生成对抗网络(GAN)。     

基于GAN网络的面部表情识别

本文为了解决姿态不变的面部表情识别即在任意姿态下的面部表情识别问题,提出了一种端到端的深度学习模型,该模型利用不同的姿态和表情进行面部图像合成扩充训练集,提高了模型的准确度,并有效地解决了姿态不变的面部表情识别问题。本文将介绍表情识别的主要过程以及模型中使用到的生成对抗网络(GAN)。     

基于面部检测和深度神经网络的面部情绪自动识别算法

面部情绪识别已成为可见光人脸识别应用的重要部 分,是光学模式识别研究中最重要的领域之一。为了进一步实现可见光条件下面部情绪的自 动识别,本文结合Viola-Jones、自适应直方图均衡(AHE)、离散小波变换(DWT)和深度卷 积神经网络(CNN),提出了一种面部情绪自动识别算法。该算法使用Viola-Jones定位脸 部和五官,使用自适应直方图均衡增强面部图像,使用DWT完成面部特征提取;最后,提取 的特征直接用于深度卷积神经网络训练,以实现面部情绪自动识别。仿真实验分别在CK+数 据库和可见光人脸图像中进行,在CK+数据集上收获了97%的平均准确 率,在可见光人脸图像测试中也获得了95%的平均准确率。实验结果 表明,针对不同的面部五官和情绪,本文算法能够对可见光面部特征进行准确定位,对可见 光图像信息进行均衡处理,对情绪类别进行自动识别,并且能够满足同框下多类面部情绪同 时识别的需求,有着较高的识别率和鲁棒性。     

A dynamic constraint representation approach based on cross-domain dictionary learning for expression recognition

Facial expression recognition (FER) is an active research area that has attracted much attention from both academics and practitioners of different fields. In this paper, we investigate an interesting and challenging issue in FER, where the training and testing samples are from a cross-domain dictionary. In this context, the data and feature distribution are inconsistent, and thus most of the existing recognition methods may not perform well. Given this, we propose an effective dynamic constraint representation approach based on cross-domain dictionary learning for expression recognition. The proposed approach aims to dynamically represent testing samples from source and target domains, thereby fully considering the feature elasticity in a cross-domain dictionary. We are therefore able to use the proposed approach to predict class information of unlabeled testing samples. Comprehensive experiments carried out using several public datasets confirm that the proposed approach is superior compared to some state-of-the-art methods.     

Multi-manifold metric learning for face recognition based on image sets

In this paper, we propose a new multi-manifold metric learning (MMML) method for the task of face recognition based on image sets. Different from most existing metric learning algorithms that learn the distance metric for measuring single images, our method aims to learn distance metrics to measure the similarity between manifold pairs. In our method, each image set is modeled as a manifold and then multiple distance metrics among different manifolds are learned. With these distance metrics, the intra-class manifold variations are minimized and inter-class manifold variations are maximized simultaneously. For each person, we learn a distance metric by using such a criterion that all the learned distance metrics are person-specific and thus more discriminative. Our method is extensively evaluated on three widely studied face databases, i.e., Honda/UCSD database, CMU MoBo database and YouTube Celebrities database, and compared to the state-of-the-arts. Experimental results are presented to show the effectiveness of the proposed method.     

Frontalization and adaptive exponential ensemble rule for deep-learning-based facial expression recognition system

Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorithms: feature-based and convolutional neural network (CNN)-based algorithms. The CNN is a powerful classifier, however, without proper auxiliary techniques, its performance may be limited. In this study, we improve the CNN-based FER system by utilizing face frontalization and the hierarchical architecture. The frontalization algorithm aligns the face by in-plane or out-of-plane, rotation, landmark point matching, and removing background noise. The proposed adaptive exponentially weighted average ensemble rule can determine the optimal weight according to the accuracy of classifiers to improve robustness. Experiments on several popular databases are performed and the results show that the proposed system has a very high accuracy and outperforms state-of-the-art FER systems.     

姿势内核学习融合决策森林的在线手势识别算法

针对在线姿势识别中来自流行深度传感器的噪声数据影响识别鲁棒性的问题,提出了一种基于姿势内核学习融合决策森林方法.首先,将使用骨架关节角表示每种姿势;然后,利用多类SVM分类器获得姿势内核;最后,利用决策森林实时标记关键姿势序列,根据关键姿势序列完成识别.实验结果表明,本方法的识别率可高达99.3％,相比几种较为先进的识别方法,本文方法具有更好的识别鲁棒性,并且在一定程度上降低了识别所耗时间.     

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.     

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。     

适用于彩色图像人脸识别的字典学习算法

现有的基于稀疏表示的人脸识别算法在识别前需要将彩色人脸图像转换成灰度人脸图像,这样虽然提高了运算速度,但忽视了不同色彩通道数据本身所包含的信息及它们之间的相关性。为了利用不同通道间相关性,基于标签一致的K奇异值分解（ LC-KSVD)字典学习算法,提出了一种适用于彩色图像人脸识别的字典学习算法。该算法将RGB通道数据顺序排列成列向量,并在稀疏编码的环节中,对正交匹配追踪（ OMP)算法的内积计算准则进行修正,以此提高字典原子的色彩表达能力。在彩色人脸数据库上进行实验,结果表明：所提出的字典学习算法能够有效地提高识别率。