基于DDCT与TCSVD的人脸特征提取与识别算法

提出一种基于分块离散余弦变换(DCT)与奇异值分解阈值压缩(TCSVD)的人脸特征提取与识别算法。该算法对人脸图像进行分块DCT变换,根据图像块位置和能量分布选择不同的DCT高低频分量构建特征矩阵,通过对每个图像块的特征矩阵进行SVD阈值压缩和特征组合来构建人脸鉴别特征,并利用分类器进行特征分类与识别。AR人脸库上的实验结果验证了该算法的有效性。     

小波变换在人脸图像融合中的应用

为了提高人脸检测图像的质量,本文提出了一种基于小波变换模型的人脸图像融合处理算法,该算法将两幅或者多福不同光照条件的人脸图像进行融合处理。该算法不仅可以改善光照条件对图像质量的影响,并且有效的消除了人脸图像的模糊边界。通过与其他同类算法的实验表明,本文提出的算法能有效地改善图像质量,减少不同光照条件对人脸检测准确率的影响。     

基于图像扩散速度模型和纹理信息的人脸活体检测

为了解决人脸身份认证中的欺诈问题,提出了一种基于图像扩散速度模型和纹理信息的人脸活体检测算法。真实人脸和虚假人脸图像的空间结构不同,为了提取这种差异特征,该方法使用各向异性扩散增强图像的边缘信息。然后,将原始图像与扩散后图像的差值作为图像的扩散速度,并构建扩散速度模型。接着使用局部二值算法提取图像扩散速度特征并训练分类器。真实人脸图像和虚假人脸图像之间存在很多差异特征,为了进一步提高人脸活体检测算法的泛化能力,该方法同时提取人脸图像的模糊程度特征和色彩纹理特征,通过特征矩阵级联的方法将两种特征进行融合,并训练另一个分类器。最后根据分类器输出概率加权融合的结果做出判决。实验结果表明,该算法能够快速有效地检测出虚假的人脸图像。     

基于本征图像分解的人脸光照迁移算法

提出了一种基于本征图像分解的人脸光照迁移算法.首先,针对本征图像分解效果不彻底的情况,提出了一种改进的本征图像分解方法.在此基础上,为了保持人脸细节特征,提出了一种基于边缘保留的光照滤波算法,对参照人脸进行光照迁移至目标人脸,最后融合目标材质图像与滤波后光照图像进行人脸重光照.实验结果表明,与已有算法相比,该算法能够很好地保留迁移后的人脸肤色,并且所生成的重光照效果更准确、自然.     

基于分块小波变换与奇异值阈值压缩的人脸特征提取与识别算法

提出了一种基于分块小波变换与奇异值阈值压缩的人脸特征提取与识别算法.该方法首先对人脸图像进行分块小波变换,并根据图像块的位置分布选取不同的频率分量,然后对该分量进行奇异值阈值压缩与特征融合,最后在ORL人脸库上利用最近邻分类器对该特征进行分类识别,验证了算法的有效性.     

基于人眼定位的快速人脸检测及归一化算法

本文提出了一种基于人眼定位的有效人脸图像归一化算法。该算法首先利用Harr特征的快速检测法从图像中检测出人脸的大致位置，然后基于人脸的几何分布特征和灰度信息特征准确检测人眼瞳孔位置建立人脸坐标系，最后对人脸图像作旋转、尺度和灰度的归一化校正。实验结果证明，该算法能够有效并准确地检测和校正人脸，可以显著提高识别率。     

基于迭代多级中值滤波的人脸美化算法

提出了一种基于图像二值化之后利用迭代的非线性多级中值滤波器的人脸美化算法。该算法首先利用二值化算法粗略地分离出人脸图像中的眼睛、鼻子、嘴巴等特征区域;然后对图像中的其它区域进行迭代多级中值滤波处理。通过与其它人脸美化算法实验结果的比较表明,该算法可以较有效地去除人脸图像中的如斑点、皱纹等不理想因素并且保留原图像的特征细节信息。     

基于改进的肤色空间和几何特征的快速人脸检测研究

提出一种基于肤色分割和几何特征相结合的人脸检测算法。该算法对图像进行光照补偿预处理,增强图像对比度;采用YCg Cr色彩空间的人脸肤色模型,对图像进行肤色分割,得出肤色轮廓;利用人脸的几何特征筛选出人脸区域。实验结果表明,该算法检测率高,实时性好,误检率低。     

多注意力域的稠密连接人脸超分算法

人脸超分辨率重建是一种对低分辨率人脸处理获取对应高分辨率人脸的低成本技术,又称人脸幻生。为了使重建的人脸图像有更清晰的细节纹理,通过对基于深度学习的人脸超分算法的研究,提出了基于注意力机制的稠密连接人脸超分算法。该算法主要由特征提取和图像重建两个部分组成,通过同时关注特征通道域和空间域的信息,创建了多注意力域模块MADM（Multi Attention Domain Module）。其中,通过改变信道和空间上的相互关系和权重,自适应地对特征进行加权重组,并且使用密集的稠密连接和长短连接将不同层的特征融合在一起,实现提升网络性能。实验结果验证了该算法的正确性;并与现有算法比较,表明了该算法的优越性能,重建的人脸图像具有更清晰的纹理细节特征。     

基于差空间的最大散度差鉴别分析及人脸识别

提出了一种新的基于差空间的最大散度差鉴别特征抽取方法。该方法首先通过构造人脸图像的差空间，部分地消除由于光照条件不同而引起的人脸图像的不稳定性，然后采用最大散度差鉴别准则函数进行最优鉴别特征的抽取，这样从根本上避免了传统的Fisher线性鉴别分析中存在的“小样本问题”。最后，在ORL标准人脸库和Yale人脸库上的实验结果验证了本文算法的有效性。     

Orthogonal discriminant vector for face recognition across pose

Recognizing face images across pose is one of the challenging tasks for reliable face recognition. This paper presents a new method to tackle this challenge based on orthogonal discriminant vector (ODV). The result of our theoretical analysis shows that an individual’s probe image captured with a new pose can be represented by a linear combination of his/her gallery images. Based on this observation, in contrast to the conventional methods which model face images of different individuals on a single manifold, we propose to model face images of different individuals on different linear manifolds. The contribution of our approach includes: (1) to prove that the orthogonality to ODVs is a pose-invariant feature.; (2) to categorize each person with a set of ODVs, where his/her face images posses zero projections while other persons’ images are characterized by maximum projections; (3) to define a metric to measure the distance between a face image and an ODV, and classify the face images based on this metric. Our experimental results validate the feasibility of modeling the face images of different individuals on different linear manifolds. The proposed method achieves higher accuracy on face recognition and verification than the existing techniques.     

FaceEncAuth：基于FaceNet和国密算法的人脸识别隐私安全方案

人脸识别中,人脸特征作为生物特征的一种,具有唯一性、不可撤销性,一旦遭到攻击、篡改或泄露,用户隐私安全将面临巨大威胁。针对这一问题,提出一种基于深度学习和加密算法的人脸识别隐私安全方案。该方案中,利用FaceNet深度学习算法来高效提取人脸特征,协调生物特征模糊性与密码系统的精确性,采用CKKS全同态加密算法进行人脸识别密文域的运算,通过国密SM4算法增强人脸特征密文抵抗恶意攻击的能力,利用其对称密码的性质兼顾了安全性和运算效率,而SM9非对称密码算法则用于SM4算法对称密钥的管理。实验结果及分析表明,该方案在不影响人脸识别准确率、效率的前提下提高了数据传输、存储和比对的安全性。     

Facial expression recognition based on dual-feature fusion and improved random forest classifier

Facial expression recognition (FER) is an important means for machines to understand the changes in the facial expression of human beings. Expression recognition using single-modal facial images, such as gray scale, may suffer from illumination changes and the lack of detailed expression-related information. In this study, multi-modal facial images, such as facial gray scale, depth, and local binary pattern (LBP), are used to recognize six basic facial expressions, namely, happiness, sadness, anger, disgust, fear, and surprise. Facial depth images are used for robust face detection initially. The deep geometric feature is represented by point displacement and angle variation in facial landmark points with the help of depth information. The local appearance feature, which is obtained by concatenating LBP histograms of expression-prominent patches, is utilized to recognize those expression changes that are difficult to capture by only the geometric changes. Thereafter, an improved random forest classifier based on feature selection is used to recognize different facial expressions. Results of comparative evaluations in benchmarking datasets show that the proposed method outperforms several state-of-the-art FER approaches that are based on hand-crafted features. The capability of the proposed method is comparable to that of the popular convolutional neural-network-based FER approach but with fewer demands for training data and a high-performance hardware platform.     

Robust face recognition against expressions and partial occlusions

Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis(ICA) features. These local features are acquired using locally lateral subspace(LLS) strategy.Then, through linear discriminant analysis(LDA) we investigate the intraclass and interclass representation of each local ICA feature and express each feature s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding(LLE) along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE,FERET and CK+ is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.     

Facial expressions in American sign language: Tracking and recognition

This paper presents work towards recognizing facial expressions that are used in sign language communication. Facial features are tracked to effectively capture temporal visual cues on the signers' face during signing. Face shape constraints are used for robust tracking within a Bayesian framework. The constraints are specified through a set of face shape subspaces learned by Probabilistic Principal Component Analysis (PPCA). An update scheme is also used to adapt to persons with different face shapes. Two tracking algorithms are presented, which differ in the way the face shape constraints are enforced. The results show that the proposed trackers can track facial features with large head motions, substantial facial deformations, and temporary facial occlusions by hand. The tracked results are input to a recognition system comprising Hidden Markov Models (HMM) and a support vector machine (SVM) to recognize six isolated facial expressions representing grammatical markers in American sign language (ASL). Tracking error of less than four pixels (on 640×480 videos) was obtained with probability greater than 90%; in comparison the KLT tracker yielded this accuracy with 76% probability. Recognition accuracy obtained for ASL facial expressions was 91.76% in person dependent tests and 87.71% in person independent tests.     

Face recognition in low resolution thermal images

This paper proposes an accurate, rotation invariant, and fast approach for detection of facial features from thermal images. The proposed approach combines both appearance and geometric information to detect the facial features. A texture based detector is performed using Haar features and AdaBoost algorithm. Then the relation between these facial features is modeled using a complex Gaussian distribution, which is invariant to rotation. Experiments show that our proposed approach outperforms existing algorithms for facial features detection in thermal images. The proposed approach’s performance is illustrated in a face recognition framework, which is based on extracting a local signature around facial features. Also, the paper presents a comparative study for different signature techniques with different facial image resolutions. The results of this comparative study suggest the minimum facial image resolution in thermal images, which can be used in face recognition. The study also gives a guideline for choosing a good signature, which leads to the best recognition rate.     

Manifold‐Based 3D Face Caricature Generation with Individualized Facial Feature Extraction

Caricature is an interesting art to express exaggerated views of different persons and things through drawing. The face caricature is popular and widely used for different applications. To do this, we have to properly extract unique/specialized features of a person's face. A person's facial feature not only depends on his/her natural appearance, but also the associated expression style. Therefore, we would like to extract the neutural facial features and personal expression style for different applicaions. In this paper, we represent the 3D neutral face models in BU–3DFE database by sparse signal decomposition in the training phase. With this decomposition, the sparse training data can be used for robust linear subspace modeling of public faces. For an input 3D face model, we fit the model and decompose the 3D model geometry into a neutral face and the expression deformation separately. The neutral geomertry can be further decomposed into public face and individualized facial feature. We exaggerate the facial features and the expressions by estimating the probability on the corresponding manifold. The public face, the exaggerated facial features and the exaggerated expression are combined to synthesize a 3D caricature for a 3D face model. The proposed algorithm is automatic and can effectively extract the individualized facial features from an input 3D face model to create 3D face caricature.     

基于小波变换和多特征融合算法的人脸识别

提出了基于小波变换和多特征融合算法的人脸识别方法。该方法先对原始人脸图像进行简单加权小波变换以降低维数,施行改进的模块二维主成分分析（M2DPCA）抽取特征,再进行加权最大散度差鉴别分析（WMSD）得到最终的特征图像,采用最近邻分类器对人脸分类识别。该方法不仅利用了人脸图像的局部特征和类别信息,而且避免了矩阵的奇异值分解可能遇到的问题。在ORL人脸库上实验,以验证该方法的有效性。     

基于TI-SPCA的人脸自动对齐及识别框架

随着人脸识别算法在众多应用领域的迅猛发展,作为人脸检测和人脸识别中间步骤的人脸对齐算法日益受到重视。针对平面内的人脸图像旋转问题,提出一个基于TI-SPCA（Transformation Invariant Symmetrical Principal Components Analysis）的人脸自动对齐方法及其识别框架。不同于传统的人眼对齐方法,TI-SPCA通过最小化重构图像和扭曲图像之间的误差得到一个旋转不变的特征空间,最终实现无人为干涉的全自动对齐。为了将其性能与人眼对齐方法的性能进行比较,并展示其优势,文中分别在ORL数据库和FERET数据库上通过两种不同对齐方法的输出图像从视觉效果上直观地展现。进一步地,为了验证对齐后的图像在识别算法中的有效性,结合三种距离函数和四种局部算子进行了对比实验,实验结果表明了基于TI-SPCA的全自动对齐方法在人脸识别中的有效性。     

Wavelets-based facial expression recognition using a bank of support vector machines

A human face does not play its role in the identification of an individual but also communicates useful information about a person’s emotional state at a particular time. No wonder automatic face expression recognition has become an area of great interest within the computer science, psychology, medicine, and human–computer interaction research communities. Various feature extraction techniques based on statistical to geometrical data have been used for recognition of expressions from static images as well as real-time videos. In this paper, we present a method for automatic recognition of facial expressions from face images by providing discrete wavelet transform features to a bank of seven parallel support vector machines (SVMs). Each SVM is trained to recognize a particular facial expression, so that it is most sensitive to that expression. Multi-classification is achieved by combining multiple SVMs performing binary classification using one-against-all approach. The outputs of all SVMs are combined using a maximum function. The classification efficiency is tested on static images from the publicly available Japanese Female Facial Expression database. The experiments using the proposed method demonstrate promising results.