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。     

基于协作表示残差融合的3维人脸识别

目的 针对2维人脸难以克服光照、表情、姿态等复杂问题,提出了一种基于协作表示残差融合的新算法.方法 协作表示分类算法是将所有类的训练图像一起协作构成字典,通过正则化最小二乘法代替1范数求解稀疏系数,减小了计算的复杂度,由此系数重构测试人脸,根据重构误差最小原则,对测试人脸正确分类.该方法首先在3维人脸深度图上提取Gabor特征和Geodesic特征,然后在协作表示算法的基础上融合两者的残差信息,作为最终差异性度量,最后根据融合残差最小原则,进行人脸识别.结果 在不同的训练样本、特征维数条件下,在CIS和Texas 2 个人脸数据库上,本文算法的识别率可分别达到94.545%和99.286%.与Gabor-CRC算法相比,本文算法的识别率平均高出了10%左右.结论 在实时成像系统采集的人脸库和Texas 3维人脸库上的实验结果表明,该方法对有无姿态、表情、遮挡等变化问题具有较好的鲁棒性和有效性.     

Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios.     

基于特征分块的三维人脸重建和识别

针对传统三维人脸重建算法效率低且难以满足实际应用的缺陷,提出一种基于特征分块的三维人脸重建算法,并将此算法应用到三维人脸识别中,实现了基于特征分块的加权三维人脸识别。首先,利用基于平面模板的非均匀重采样法对原始数据进行归一化;其次,采用主动形状模型(ASM)算法对三维人脸和二维人脸图像进行特征定位和特征分块;然后,利用基于分块主元分析(PCA)的稀疏形变模型算法实现每个人脸分块的三维重建;最后,实现了此算法在三维人脸识别中的应用。实验表明,此重建算法具有较高的精度和重建效率,还可以达到全局最优,并且可以提高三维人脸的识别率。     

人脸样貌独立判别的协作表情识别算法

为了降低样貌、姿态、眼镜以及表情定义不统一等因素对人脸表情识别的影响,提出一种人脸样貌独立判别的协作表情识别算法。首先,采用自动的人脸检测算法定位、对齐视频每帧的人脸区域,并从人脸视频序列中选择峰值表情的人脸;然后,采用峰值人脸与某个表情类内的所有人脸产生表情类内差异人脸信息,并通过计算峰值表情人脸与表情类内差异人脸的差异信息获得协作的表情表示;最终,采用基于稀疏的分类器与表情表示决定每个人脸表情的标签。采用欧美与亚洲人脸的数据库进行仿真实验,结果表明本算法获得了较好的表情识别准确率,对不同样貌、佩戴眼镜的人脸样本也具有较好的识别效果。     

基于对称Gabor特征和稀疏表示的人脸识别

受启发于人脸近似对称的先验知识,提出一种基于对称Gabor特征的稀疏表示算法并成功运用于人脸识别。首先把人脸图像进行镜像变换得到其镜像图像,进而将人脸分解为奇偶对称脸。在奇偶对称脸上分别提取Gabor特征,得到Gabor奇偶对称特征。通过一个加权因子,将奇偶特征融合生成新的特征。最后用这种新的特征构成超完备字典进行稀疏表示人脸分类。在人脸数据库AR和FERET上的实验结果表明所提算法在人脸有表情、姿势和光照变化情况下仍能获得较高的识别率。     

基于子空间稀疏系数的表情识别方法

提出了一种运用SRC（Sparse Representation based Classification）在个体子空间里,进行表情识别的新方法。用Gabor滤波器,提取表情图像的特征。进行稀疏分解,得到稀疏表示系数。根据稀疏系数确定待测图像所在的子空间,在子空间里,完成表情识别。这种方法较好地避免了不同个体对表情识别的干扰,从而提高了表情识别的正确率。在Cohn-Kanade和JAFFE人脸库上的表情识别实验表明,该方法对表情识别非常有效。     

人脸的层次化描述模型及识别研究

人脸自动识别是一个困难但有重要意义的工作。文中提出了一种基于人脸层次化描述的识别方法。该方法首先对人脸进行快速准确的特征定位及标准化,然后采用主元分析神经网络分别对定位的人脸及其特征区域进行最佳特征提取,从而得到人脸在低分辨率和较高分辨率上的两层特征描述用以识别,具有识别率高、特征数据量适中、可用于大量人像识别等特点。此方法在１３００幅人像上进行了测试,结果表明其在人脸转动、表情变化或入脸未经训练     

Recognizing faces using Adaptively Weighted Sub-Gabor Array from a single sample image per enrolled subject

In this paper, we propose a new approach for face representation and recognition based on Adaptively Weighted Sub-Gabor Array (AWSGA) when only one sample image per enrolled subject is available. Instead of using holistic representation of face images which is not effective under different facial expressions and partial occlusions, the proposed algorithm utilizes a local Gabor array to represent faces partitioned into sub-patterns. Especially, in order to perform matching in the sense of the richness of identity information rather than the size of a local area and to handle the partial occlusion problem, the proposed method employs an adaptively weighting scheme to weight the Sub-Gabor features extracted from local areas based on the importance of the information they contain and their similarities to the corresponding local areas in the general face image. An extensive experimental investigation is conducted using AR and Yale face databases covering face recognition under controlled/ideal condition, different illumination condition, different facial expression and partial occlusion. The system performance is compared with the performance of four benchmark approaches. The promising experimental results indicate that the proposed method can greatly improve the recognition rates under different conditions.     

Real-time recovery of moving 3D faces for emerging applications

Existing face imaging systems are not suitable to meet the face representation and recognition demands for emerging applications in areas such as interactive gaming, enhanced learning environments and directed advertising. This is mainly due to the poor capture and characterisation of facial data that compromises their spatial and temporal precision. For emerging applications it is not only necessary to have a high level of precision for the representation of facial data, but also to characterise dynamic faces as naturally as possible and in a timely manner. This study proposes a new framework for capturing and recovering dynamic facial information in real-time at significantly high order of spatial and temporal accuracy to capture and model subtle facial changes for enhanced realism in 3D face visualisation and higher precision for face recognition applications. We also present a novel, fast, and robust correspondence mapping approach for 3D registration of moving 3D faces.     

Illuminant-Dependence of Von Kries Type Quotients

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods.The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.     

Robust Video Face Recognition Under Pose Variation

With the abundance of video data, the interest in more effective methods for recognizing faces from surveillance videos has grown. However, most algorithms proposed in this field have an assumption that each image set lies in a single linear subspace, or a mixture of linear subspaces. As a result, 3-dimensional shape information, which leads to the nonlinear transformation of face images, is ignored. This paper proposes a robust video face recognition across pose variation in video (RVPose) based on sparse representation. The key idea is performing alignment and recognition based on sparse representation simultaneously. Moreover, by considering that multi-pose faces of the same subject possess the same texture and 3-dimensional shape, RVPose aligns a sequence of faces with pose variations simultaneously, which is reduced to a 3-dimensional shape-constrained video alignment problem. Finally, aligned video sequence is recognized based on sparse represent. Experiments conducted on public video datasets demonstrate the effectiveness of the proposed algorithm.     

基于高阶结构约束的稀疏人脸识别算法

为了挖掘人脸稀疏表示的高阶结构信息,从而获得更精确的人脸分类判别信息,提出一种新的基于高阶结构约束的稀疏表示的人脸识别算法。在提取人脸图像的Gabor特征后,选取适当的训练样本构建稀疏线性编码模型,利用样本特征间的高阶结构信息约束和最优化求解测试样本的稀疏系数,将稀疏系数作为最终特征对人脸进行识别,实验结果表明,与结构约束稀疏表示和非结构约束稀疏表示的方法比较,人脸的高阶结构稀疏表示能显著提高人脸的识别准确率。     

Face recognition by applying wavelet subband representation and kernel associative memory

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.     

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.     

改进Retinex和稀疏表示的光照人脸识别

为了提高光照变化条件下的人脸识别率,针对当前人脸识别方法存在的缺陷,提出了一种改进Retinex算法和稀疏表示相融合的光照人脸识别方法。首先对Retinex算法的不足进行改进,并应用于人脸图像预处理中,消除光照对人脸识别的干扰,然后采用稀疏表示提取人脸特征向量,并采用投票方式实现人脸识别,最后通过3个标准人脸数据库对方法的性能进行测试。结果表明,该方法不仅提高了人脸识别率,而且缩短了人脸识别时间,对光照具有较好的鲁棒性。     

Anthropometric 3D Face Recognition

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.     

基于压缩金字塔核稀疏表示的人脸识别

人脸识别中光照、伪装及姿态等变化一直是富有挑战性的问题,其中特征提取是很关键的一步。为提高人脸识别率,结合压缩感知和空间金字塔模型,本文提出了一种新的特征提取方法,首先用尺度不变特征变换算法提取图像特征,然后与随机生成的字典进行稀疏编码,再用金字塔模型分层提取不同尺度空间的特征,并用最大池融合特征,最后运用核稀疏表示分类。在Extended Yale B,AR 和CMU PIE人脸数据库上的实验结果表明,该方法对于人脸图像的光照、伪装及姿态等变化有较强的鲁棒性,而且该算法有较快的运行速度。