数据增广下的人脸识别研究

随着计算机技术的发展和应用,人脸识别技术以其具有的非强制性、非接触性、并发性等优势得到了越来越广泛的应用。大规模数据是提高基于深度学习人脸识别准确率的关键因素,但往往数据不易获得,并且存在训练数据缺乏测试数据样本的情况,如模糊、亮度失真和腐蚀感画质样本等。针对训练数据缺乏问题,提出了利用滤波、亮度调节和腐蚀操作3种传统图像处理方法10种增广方式增加数据量和数据的多样性,进而提高识别算法的性能。将原始数据和增广数据作为训练数据训练模型,选择从不同地方拍摄的视频上截取的人脸图像组成了四个测试集,实验结果表明,增广数据与测试集样本存在一致性时,增广方式对提升识别性能都有一定的效果,其中最好的效果是对图像整体调亮时在一个测试集上的识别率提高了4.02%。     

Model-Driven Domain Adaptation on Product Manifolds for Unconstrained Face Recognition

Many classification algorithms see a reduction in performance when tested on data with properties different from that used for training. This problem arises very naturally in face recognition where images corresponding to the source domain (gallery, training data) and the target domain (probe, testing data) are acquired under varying degree of factors such as illumination, expression, blur and alignment. In this paper, we account for the domain shift by deriving a latent subspace or domain, which jointly characterizes the multifactor variations using appropriate image formation models for each factor. We formulate the latent domain as a product of Grassmann manifolds based on the underlying geometry of the tensor space, and perform recognition across domain shift using statistics consistent with the tensor geometry. More specifically, given a face image from the source or target domain, we first synthesize multiple images of that subject under different illuminations, blur conditions and 2D perturbations to form a tensor representation of the face. The orthogonal matrices obtained from the decomposition of this tensor, where each matrix corresponds to a factor variation, are used to characterize the subject as a point on a product of Grassmann manifolds. For cases with only one image per subject in the source domain, the identity of target domain faces is estimated using the geodesic distance on product manifolds. When multiple images per subject are available, an extension of kernel discriminant analysis is developed using a novel kernel based on the projection metric on product spaces. Furthermore, a probabilistic approach to the problem of classifying image sets on product manifolds is introduced. We demonstrate the effectiveness of our approach through comprehensive evaluations on constrained and unconstrained face datasets, including still images and videos.     

面向中文命名实体识别任务的数据增强

In low-resource natural language processing (NLP) tasks, the existing data is not enough to train an ideal deep learning model. Text data augmentation is an effective method to improve the training effect of such tasks. This paper proposes a group of data augmentation methods based on instance substitution for the task of Chinese named entity recognition. A named entity in the training sample can be replaced by another entity of the same kind without changing the label. The specific algorithms include: 1) crossover substitution between existing entities; 2) synonymous replacement of entity components; 3) automatic generation of Chinese names. These methods are applied to PeopleDailyNER and CLUENER2020 datasets respectively, and the augmentation data is used to train the BERT+CRF model. The experimental results show that the F1 value of the model can be improved by about 10% and 7% respectively on the two datasets with only adding the same amount of augmentation data as the original data under the condition of small samples, and it also has a significant improvement when the training samples increase.     

用于脑电数据增强和情绪识别的自注意力GAN

针对脑电信号（electroencephalogram,EEG）情绪识别中数据稀缺及由此导致的情感分类精度不高的问题,提出了一个引入自注意力机制的条件Wasserstein生成对抗网络（SA-cWGAN）,通过自注意力模块从训练数据学习长时上下文相关的全局特征,采用Wasserstein距离和梯度惩罚的Lipschitz约束对网络的损失函数进行优化,进而生成高质量的EEG数据对原有训练集进行增强。所提方法分别在DEAP和SEED数据集上进行了大量的二分类和三分类对比实验,生成了与EEG训练数据分布接近的微分熵（DE）和功率谱密度（PSD）特征,以此来增强EEG训练数据集,采用SVM分类器对增强后的EEG特征进行情绪分类。实验结果表明,在DEAP数据集上的唤醒度和效价维度下,增强后的DE、PSD特征较原有DE、PSD特征二分类准确率分别提高了16.63、17.55个百分点和6.48、8.34个百分点;在SEED数据集下,三分类准确率分别提高了4.64、5.18个百分点,证明所提方法生成的特征具有良好的鲁棒性,也表明通过对GAN网络引入自注意力机制生成的特征增强原有训练数据集能够有效提高E...     

面向新类型人名识别的数据增强方法

人名识别常被作为命名实体识别任务的一部分,与其他类型的实体同时进行识别。当前使用NER方法的人名识别依赖于训练语料对特定类型人名的覆盖,在遇到新类型人名时性能显著下降。针对上述问题,该文提出了一种基于数据增强(data augmentation)的方法,使用新类型人名实体替换的策略来生成伪训练数据,该方法能够有效提升系统对新类型人名的识别性能。为了选择有代表性的特定类型人名实体,该文提出了贪心的代表性子类型人名选择算法。在使用1998年《人民日报》数据自动生成的伪测试数据和人工标注的新闻数据的测试结果中,多个模型上人名识别的F₁值分别提升了至少12个百分点和6个百分点。     

基于注意力引导数据增强的车型识别

车型识别在智能交通系统中发挥着重要作用。受车辆数据不足、车辆类间差异小等因素的影响,传统车型识别方法未充分利用车辆鉴别性区域的特征,导致识别准确率降低。提出一种基于注意力模块引导数据增强的车型识别方法。将ResNet-50作为骨干网络提取车辆特征,同时在网络的每个残差块后均嵌入坐标注意力模块,编码成一对方向感知和位置敏感的注意力图,以增强车辆鉴别性区域的特征表达。在此基础上,利用双线性注意力汇集操作生成增强特征图,通过对增强特征图进行注意力裁剪和注意力擦除,获取具有强鉴别性的增强数据。在Stanford Cars车辆数据集上的实验结果验证了该方法的有效性,结果表明,该方法的车型识别准确率达到94.86%,与RA-CNN、MA-CNN、WS-DAN+Inception-v3等方法相比,能够有效提高车型识别准确率和数据增强效率。     

基于数据增强的中文隐式篇章关系识别方法

由于缺乏显式连接词,隐式篇章关系识别是一个具有挑战性的任务.文中提出了一种结合主动学习和多任务学习来间接扩充隐式篇章关系训练数据的隐式篇章关系识别方法,旨在在增强训练数据的同时尽量少地引入伪隐式篇章关系数据中的噪声.首先,基于BERT模型通过主动学习方法的分类不确定性来选择部分显式篇章关系样本;然后,移除显式篇章关系数...     

基于数据融合的贝叶斯人脸识别方法

分析了反对称双正交小波分解细节系数在光照条件变化下的稳定性，提出了一种基于数据融合的贝叶斯人脸识别方法，利用AR人脸图像库进行了对比实验，实验结果验证了本文方法的有效性。     

基于双层虚拟视图和支持向量的人脸识别方法

针对训练样本较少情况下的人脸识别问题,该文提出基于生成视图和支持向量机的识别方法．在人脸识别的实际应用中,处理的人脸图像,每类往往只有很少的样本,以至于不能充分表达样本的实际分布,需要对训练样本的数据进行有效地扩充．为此首先通过对人脸图像中眼睛中心位置的扰动,利用面像模板,自动生成该人脸的多个虚拟人脸图像,并与原图像一起形成第一层的人脸库,然后应用Eigenface方法得到人脸的特征数据,按照每个类的样本数据分布,应用内插法和外推法进行第二层次的扩充．在ICT-YCNC和UMIST人脸库中应用Multi-Class支持向量机对得到的数据进行实验,结果表明,在样本不足的条件下利用支持向量识别人脸,生成虚拟视图是一种有效的方法．     

基于生成对抗网络的人脸表情数据增强方法

基于深度学习的方法已经在人脸表情识别中取得了重大进展,然而人脸表情数据库的规模普遍不大。为了解决数据量不足的问题,提出了一种静态图像数据增强方法。在StarGAN的基础上修改重构误差实现多风格人脸表情图像转换,利用生成器由某一表情下的面部图像生成同一人其他表情的面部图像。在CK+表情库上的实验表明,该方法有利于提高人脸表情识别模型的识别率和泛化能力,同时对解决数据量不平衡的问题也有借鉴作用。     

A Novel Connectionist System for Unconstrained Handwriting Recognition

Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.     

一种非限制性的手写数字的识别方法

本文从语义,句法模式识别观点,提出一种基于模型指导的有限状态属性自动机,进行特征抽取。对每一种典型的畸变模型设计一个有限状态属性文法及其相应的属性自动机,采用自下而上和自上而下相结合的控制策略,并在低层次引入知识指导,减少了工作量和不确定性。基于上述方法实现的非限制性手写数字识别系统,经过对１１００个非限制性手写数字样本的测试,平均识别率达９５．２％,拒识率为４．６％,误识率为０．２％。     

面向时延数据的分块PCA红外人脸识别

时延数据下的高识别性能是红外人脸识别系统应用的基础,提出一种基于分块PCA的红外人脸识别方法,提高红外人脸识别系统对时延数据识别率.为了充分利用图像的局部特征,对图像进行大小分块,并对各分块图像进行主成分分析;然后,为了缓解外部环境温度对识别的影响,通过训练学习不同温度下的红外人脸数据,去除每个图像块受环境温度影响较大的特征值.最后,为了结合整体特征,并将余下的特征值组合成特征向量用于识别.实验结果表明,本文提出的方法可以提高红外人脸识别系统对时延数据的识别率.     

Color Face Recognition for Degraded Face Images

In many current face-recognition (FR) applications, such as video surveillance security and content annotation in a web environment, low-resolution faces are commonly encountered and negatively impact on reliable recognition performance. In particular, the recognition accuracy of current intensity-based FR systems can significantly drop off if the resolution of facial images is smaller than a certain level (e.g., less than 20 $times$ 20 pixels). To cope with low-resolution faces, we demonstrate that facial color cue can significantly improve recognition performance compared with intensity-based features. The contribution of this paper is twofold. First, a new metric called “variation ratio gain” (VRG) is proposed to prove theoretically the significance of color effect on low-resolution faces within well-known subspace FR frameworks; VRG quantitatively characterizes how color features affect the recognition performance with respect to changes in face resolution. Second, we conduct extensive performance evaluation studies to show the effectiveness of color on low-resolution faces. In particular, more than 3000 color facial images of 341 subjects, which are collected from three standard face databases, are used to perform the comparative studies of color effect on face resolutions to be possibly confronted in real-world FR systems. The effectiveness of color on low-resolution faces has successfully been tested on three representative subspace FR methods, including the eigenfaces, the fisherfaces, and the Bayesian. Experimental results show that color features decrease the recognition error rate by at least an order of magnitude over intensity-driven features when low-resolution faces (25 $times$ 25 pixels or less) are applied to three FR methods.      

生物特征识别技术之面像识别

与其他生物识别技术相比,面像识别技术的可接受性强,人们通常不会介意将面孔作为识别特征。所以,在科技迅速发展的今天,面像识别技术已经在国家安全、军事安全和公安、司法、民政、金融、民航、海关、及民用等领域得到了应用。本文着重从面像识别技术的发展历程、技术框架、特点与功能等多方面进行阐述,便于读者对关乎我们日常安全的自动识别技术有更全面的了解。     

面部识别U盘

日本Futen公司近日推出一款加密U盘LockFace,该产品创新的采用了面部识别技术进行加密。面部识别技术现在已经在许多数码相机、笔记本电脑等产品中开始应用,LockFace将面部识别作为认证方式来保护数据安全。用户将这款U盘连接到电     

用于人脸识别的GFC方法

Gabor-Fisher分类器(GFC)人脸识别方法,首先对面部图像进行Gabor小波处理,再对得到的增广Gabor特征向量应用增强线性辨别法模型(EFM)以得到辨别特征。该方法的新颖之处在于(1)引入了Gabor小波得到增广的特征向量更好的反映了图像的特征;(2)应用EFM在对维数进行降低的同时进行分类特征提取。该种新方法在应用于光照和面部表情变化比较大的FERET数据库时优势比较明显。     

Personalized Convolution for Face Recognition

International Journal of Computer Vision - Face recognition has been significantly advanced by deep learning based methods. In all face recognition methods based on convolutional neural network...     

大数据在人脸识别在智慧城市的应用

目前人脸识别技术无法满足智慧城市高增长率人脸识别需求,因此提出大数据在人脸识别在智慧城市的应用。以智慧城市应用需求为基础,建立大数据人脸识别模型,处理过程中人脸特征光照、距离以动态特征等影响因素,建立数据库,对数据低频处理后信息分类,完成人脸特征识别。设计仿真实验,对比大数据人脸识别方法、OpenCV人脸识别方法以及BP神经网络人脸识别方法的识别准确率,证明研究的有效性。