Privacy preserving security using biometrics in cloud computing

Cloud computing and the efficient storage provide new paradigms and approaches designed at efficiently utilization of resources through computation and many alternatives to guarantee the privacy preservation of individual user. It also ensures the integrity of stored cloud data, and processing of stored data in the various data centers. However, to provide better protection and management of sensitive information (data) are big challenge to maintain the confidentiality and integrity of data in the cloud computation. Thus, there is an urgent need for storing and processing the data in the cloud environment without any information leakage. The sensitive data require the storing and processing mechanism and techniques to assurance the privacy preservation of individual user, to maintain the data integrity, and preserve confidentiality. Face recognition has recently achieved advancements in the unobtrusive recognition of individuals to maintain the privacy-preservation in the cloud computing. This paper emphasizes on cloud security and privacy issues and provides the solution using biometric face recognition. We propose a biometrics face recognition approach for security and privacy preservation of cloud users during their access to cloud resources. The proposed approach has three steps: (1) acquisition of face images (2) preprocessing and extraction of facial feature (3) recognition of individual using encrypted biometric feature. The experimental results establish that our proposed recognition approach can ensure the privacy and security of biometrics data.

     

Facial expression recognition – A real time approach

Face localization, feature extraction, and modeling are the major issues in automatic facial expression recognition. In this paper, a method for facial expression recognition is proposed. A face is located by extracting the head contour points using the motion information. A rectangular bounding box is fitted for the face region using those extracted contour points. Among the facial features, eyes are the most prominent features used for determining the size of a face. Hence eyes are located and the visual features of a face are extracted based on the locations of eyes. The visual features are modeled using support vector machine (SVM) for facial expression recognition. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 98.5%.     

基于小波变换和去噪模型的光照不变人脸识别

针对正面光照人脸识别的难点,提出了一种应用小波变换和去噪模型的光照不变人脸识别算法。利用对图像的高频小波系数进行处理并运用去噪模型,提取光照人脸图像中的光照不变量,同时增强图像边缘特征,这有利于提取的光照不变量保持更多的人脸识别信息。在Yale B和CMU PIE人脸库上的实验结果表明,所提算法可以显著提高光照人脸图像的识别率。     

基于仿射包和伪Zernike矩的视频人脸检索

近年来基于视频的人脸检索已成为人脸识别和检索领域最为活跃的研究方向之一。提出了一种基于仿射包结合伪Zernike矩特征的视频人脸检索算法（FRIVAP）。在视频中检测跟踪到人脸生成图像集,接着提取图像集中人脸的伪Zernike矩特征,建立特征的仿射包,通过相似度计算得到结果。经对Honda/UCSD视频数据库和自行构建的视频数据库的大量实验表明,该算法可以充分利用视频中人脸的时间和空间信息,并且对噪声、人脸姿势变化等条件下的人脸检索有较强的鲁棒性。     

基于偏最小二乘回归的人脸身份和表情同步识别方法

将偏最小二乘回归方法用于人脸身份和表情的同步识别。首先,对每幅人脸图像进行脸部特征提取以及相应的语义特征定义。在脸部特征提取方面,从每幅图像中标定出若干脸部关键点位置,并提取图像在该关键点处的Gabor小波系数（Gabor特征）以及关键点的坐标值（几何特征）,作为该图像的输入特征。语义特征则定义为该人脸图像所属的表情类别信息以及所对应的人脸身份信息。其次,利用核主成分分析(KPCA)方法对脸部Gabor特征和几何特征进行融合,使得输入特征具有更好的识别特性;最后,运用偏最小二乘回归(PLSR)方法建立脸部特征和语义特征之间的关系模型,并运用此模型对某一测试人脸图像进行表情和身份的同步识别。通过在JAFFE国际表情数据库和AR人脸数据库上的对比实验,证实了所提方法的有效性。     

A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications

Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.

     

基于子模式的Gabor特征融合的单样本人脸识别

针对传统人脸识别方法在单训练样本条件下效果不佳的缺点,提出基于子模式的Gabor特征融合方法并用于单样本人脸识别。首先采用Gabor变换抽取人脸局部信息,为有效利用面部器官的空间位置信息,将Gabor人脸图像分块构成子模式,采用最小距离分类器对各子模式分类。最后对各子模式分类结果做决策级融合得出分类结果。根据子模式构成原则和决策级融合策略不同,提出两种子模式Gabor特征融合方法。利用ORL人脸库和CAS-PEAL-R1人脸库进行实验和比较分析,实验结果表明文中方法有效提高单样本人脸识别的正确率,改善单样本人脸识别系统的性能。     

An accurate recognition of facial expression by extended wavelet deep convolutional neural network

Facial expressions are essential in community based interactions and in the analysis of emotions behaviour. The automatic identification of face is a motivating topic for the researchers because of its numerous applications like health care, video conferencing, cognitive science etc. In the computer vision with the facial images, the automatic detection of facial expression is a very challenging issue to be resolved. An innovative methodology is introduced in the presented work for the recognition of facial expressions. The presented methodology is described in subsequent stages. At first, input image is taken from the facial expression database and pre-processed with high frequency emphasis (HFE) filtering and modified histogram equalization (MHE). After the process of image enhancement, Viola Jones (VJ) framework is utilized to detect the face in the images and also the face region is cropped by finding the face coordinates. Afterwards, different effective features such as shape information is extracted from enhanced histogram of gradient (EHOG feature), intensity variation is extracted with mean, standard deviation and skewness, facial movement variation is extracted with facial action coding (FAC),texture is extracted using weighted patch based local binary pattern (WLBP) and spatial information is extracted byentropy based Spatial feature. Subsequently, dimensionality of the features are reduced by attaining the most relevant features using Residual Network (ResNet). Finally, extended wavelet deep convolutional neural network (EWDCNN) classifier uses the extracted features and accurately detects the face expressions as sad, happy, anger, fear disgust, surprise and neutral classes. The implementation platform used in the work is PYTHON. The presented technique is tested with the three datasets such as JAFFE, CK+ and Oulu-CASIA.

     

基于对比学习的细粒度遮挡人脸表情识别

和实验室环境不同, 现实生活中的人脸表情图像场景复杂, 其中最常见的局部遮挡问题会造成面部外观的显著改变, 使得模型提取到的全局特征包含与情感无关的冗余信息从而降低了判别力. 针对此问题, 本文提出了一种结合对比学习和通道-空间注意力机制的人脸表情识别方法, 学习各局部显著情感特征并关注局部特征与全局特征之间的关系. 首先引入对比学习, 通过特定的数据增强方法设计新的正负样本选取策略, 对大量易获得的无标签情感数据进行预训练, 学习具有感知遮挡能力的表征, 再将此表征迁移到下游人脸表情识别任务以提高识别性能. 在下游任务中, 将每张人脸图像的表情分析问题转化为多个局部区域的情感检测问题, 使用通道-空间注意力机制学习人脸不同局部区域的细粒度注意力图, 并对加权特征进行融合, 削弱遮挡内容带来的噪声影响, 最后提出约束损失联合训练, 优化最终用于分类的融合特征. 实验结果表明, 无论是在公开的非遮挡人脸表情数据集(RAF-DB和FER2013)还是人工合成的遮挡人脸表情数据集上, 所提方法都取得了与现有先进方法可媲美的结果.     

Holistic and partial facial features fusion by binary particle swarm optimization

This paper proposes a novel binary particle swarm optimization (PSO) algorithm using artificial immune system (AIS) for face recognition. Inspired by face recognition ability in human visual system (HVS), this algorithm fuses the information of the holistic and partial facial features. The holistic facial features are extracted by using principal component analysis (PCA), while the partial facial features are extracted by non-negative matrix factorization with sparseness constraints (NMFs). Linear discriminant analysis (LDA) is then applied to enhance adaptability to illumination and expression. The proposed algorithm is used to select the fusion rules by minimizing the Bayesian error cost. The fusion rules are finally applied for face recognition. Experimental results using UMIST and ORL face databases show that the proposed fusion algorithm outperforms individual algorithm based on PCA or NMFs.     

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。     

基于Shearlet变换和均匀局部二值模式特征的协作表示人脸识别算法

为了获得人脸图像中更丰富的纹理特征以提高人脸识别率,提出了一种基于Shearlet变换和均匀局部二值模式(ULBP)算子提取特征(Shearlet_ULBP特征)的协作表示方法--Shearlet_ULBP CRC用于人脸识别。首先,人脸图像通过Shearlet变换分解,得到多尺度多方向的幅值域图谱,再经过简单的平均融合,获得融合后的幅值域图谱;然后,通过ULBP算子结合分块的方法获得该Shearlet变换后融合图像的直方图特征;最后,结合协作表示的方法来分类所提取到的特征。该方法可以提取到图像更丰富的边缘以及纹理信息,在ORL、Extended Yale B和AR人脸数据库上进行测试,在图像无遮挡的情况下识别率都达到了99%以上,在有遮挡情况下也都达到了91%以上的识别率。实验结果表明,所提方法不仅对于光照、姿态和表情变化具备较强的鲁棒性,同时能在一定程度上处理人脸图像中存在遮挡的情形。     

采用Transformer网络的视频序列表情识别

目的 相比于静态人脸表情图像识别,视频序列中的各帧人脸表情强度差异较大,并且含有中性表情的帧数较多,然而现有模型无法为视频序列中每帧图像分配合适的权重。为了充分利用视频序列中的时空维度信息和不同帧图像对视频表情识别的作用力差异特点,本文提出一种基于Transformer的视频序列表情识别方法。方法 首先,将一个视频序列分成含有固定帧数的短视频片段,并采用深度残差网络对视频片段中的每帧图像学习出高层次的人脸表情特征,从而生成一个固定维度的视频片段空间特征。然后,通过设计合适的长短时记忆网络（long short-term memory network,LSTM）和Transformer模型分别从该视频片段空间特征序列中进一步学习出高层次的时间维度特征和注意力特征,并进行级联输入到全连接层,从而输出该视频片段的表情分类分数值。最后,将一个视频所有片段的表情分类分数值进行最大池化,实现该视频的最终表情分类任务。结果 在公开的BAUM-1s （Bahcesehir University multimodal）和RML （Ryerson Multimedia Lab）视频情感数据集上的试验结果表明,该方法分别取得了60.72%和75.44%的正确识别率,优于其他对比方法的性能。结论 该方法采用端到端的学习方式,能够有效提升视频序列表情识别性能。     

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。     

融合LLE与LDA特征的人脸识别方法

为更好提取识别的人脸特征,文章将非线性流形学习方法LLE提取的局部非线性特征与监督学习方法LDA提取的全局线性特征相结合,利用特征融合的思想,得出有利特征,进行人脸识别。经实验证明,该方法能显著提高人脸识别系统的性能。     

基于深度数据的空间人脸旋转角度估计

提出一种基于三维人脸深度数据的人脸姿态计算方法。利用人脸的深度数 据以及与其一一对应的灰度图像,根据微分几何原理和相应的曲率算法与人脸数据中的灰度 特征对人脸面部关键特征点定位,进而计算出人脸姿态在三维空间中的3 个姿态角。实验证 明该方法能在姿态变化情况下实现对人脸旋转角的准确估计,为进一步的人脸识别和表情分 析提供基础。     

A hybrid method based on dynamic compensatory fuzzy neural network algorithm for face recognition

Human face recognition skills can make simultaneous use of a variety of information from the face, including information about the age, sex, race, identity, and even current mood of the person. In this paper, a hybrid method combined Eigenface-LDA with Dynamic Compensatory Fuzzy Neural Network (DCFNN) is proposed for face recognition. Eigenfaces-LDA algorithm is used for face image of dimensionality reduction and finding a best subspace for classification, the extracted feature will be considered as the input of DCFNN. An improved Dynamic Fuzzy Neural Network is proposed by combing Dynamic Fuzzy Neural Network and Compensatory Fuzzy Neural Network to solve the problem of feature classification. The proposed method has been tested on ORL and Yale face database; the experimental results show that our method can reduce the dimension of facial features well and recognize faces that under different illumination, pose and expression accurately.     

基于小波分析与KPCA的人脸识别方法

对于一种有效的人脸识别方法,特征选择是极为重要的问题。而小波多分辨率分析可以获得对人脸识别有用的低频特征,KPCA则可用于提取人脸非线性特征。为此,本文〖BP）〗提出结合小波变换及KPCA的特点获取人脸特征,设计线性SVM分类器进行分类识别。由于KPCA中核函数的参数选择以及训练样本与测试样本的划分对分类识别有一定的影响,为了获得最优的识别效果,在UMIST人脸数据库上进行相应的实验。结果表明本方法可以获得较好的分类识别率,是一种快速、有效的人脸识别方法。