基于DCT和线性回归的人脸识别*

由于人脸图像常常因光照、姿态、表情变化及遮挡等因素的影响而具有非线性结构,在空间域直接使用线性方法就有其局限性。为此,提出了一种基于离散余弦变换和线性回归分类的人脸识别方法:通过离散余弦变换获取人脸图像的变换域特征,以减小光照、姿态变化等影响,然后再利用快速有效的线性回归方法得到识别结果。通过在几个常用人脸数据库上的测试结果表明,该方法在满足实时性的同时,能有效地增强面对这些问题的鲁棒性。     

基于PCA余像空间的ICA混合特征人脸识别方法

为改善传统的基于特征脸的人脸识别方法在识别光照变化较大的人脸时效果不尽理想的缺陷,提出一种基于“PCA余像空间”的ICA混合特征人脸识别方法。不同于2阶PCA人脸识别方法,用独立元分析法代替主元分析法,对“PCA余像特征脸集”进行独立元特征抽取得到人脸图像基于PCA余像空间的独立元特征,并综合人脸图像的原始独立元特征得到混合特征作为最终识别的特征。实验表明,基于PCA余像空间的ICA混合特征人脸识别方法,在识别光照、表情等外界因素变化较大的人脸图像时,要优于传统的基于特征脸的识别方法、基于ICA的识别方法以及基于2阶PCA的人脸识别方法,并具有较强的适用性。     

Face recognition in hyperspectral images

Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 /spl mu/m-1.0 /spl mu/m). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression.     

Real-time face tracking and recognition using the mobile robots

The terrain is complex and dynamic in a disaster. This paper aimed at constructing a multipose-specific feature model for online face recognition and tracking in a search and rescue operation. This paper proposes an integrated multipose face tracking and recognition system mounted on an unmanned aerial vehicle (UAV). The face model is constructed online by multi-scale block local binary pattern (MB-LBP) for face recognition. The generic and specific face models are further integrated for face tracking. Mechanisms for the online update of face models to retrieve loss of face tracking are also implemented. The results show that the proposed system achieves stable and robust tracking despite uncertainties (e.g. non-rigid human face, face expression changes, different face poses, complex background, varying illumination, partial visual occlusion, and pose changes). The target loss during tracking can be retrieved correctly. In face recognition, the multipose-specific face model can achieve an accuracy (above 70%). The results demonstrate the feasibility of the proof-of-concept using UAV or ground mobile robot (GMR) for real-time face recognition and tracking.     

2D face recognition based on supervised subspace learning from 3D models

One of the main challenges in face recognition is represented by pose and illumination variations that drastically affect the recognition performance, as confirmed by the results of recent face recognition large-scale evaluations. This paper presents a new technique for face recognition, based on the joint use of 3D models and 2D images, specifically conceived to be robust with respect to pose and illumination changes. A 3D model of each user is exploited in the training stage (i.e. enrollment) to generate a large number of 2D images representing virtual views of the face with varying pose and illumination. Such images are then used to learn in a supervised manner a set of subspaces constituting the user's template. Recognition occurs by matching 2D images with the templates and no 3D information (neither images nor face models) is required. The experiments carried out confirm the efficacy of the proposed technique.     

基于三维数据与MMSV特征的二维人脸识别*

针对二维人脸识别对姿态与光照变化较为敏感的问题,提出了一种基于三维数据与混合多尺度奇异值特征MMSV(mixture of multi-scale singular value,MMSV)的二维人脸识别方法。在训练阶段,利用三维人脸数据与光照模型获取大量具有不同姿态和光照条件的二维虚拟图像,为构造完备的特征模板奠定基础;同时,通过子集划分有效地缓解了人脸特征提取过程中的非线性问题;最后对人脸图像进行MMSV特征提取,从而对人脸的全局与局部特征进行融合。在识别阶段,通过计算MMSV特征子空间距离完成分类识别。实验证明,提取到的MMSV特征包含有更多的鉴别信息,对姿态和光照变化具有理想的鲁棒性。该方法在WHU-3D数据库上取得了约98.4%的识别率。     

Independent component analysis in a local facial residue space for face recognition

In this paper, we propose an Independent Component Analysis (ICA) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most Principal Component Analysis (PCA) based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the “residual face space”. We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations.     

结合形变模型与图像修复的人脸姿态矫正

目的 人脸姿态偏转是影响人脸识别准确率的一个重要因素,本文利用3维人脸重建中常用的3维形变模型以及深度卷积神经网络,提出一种用于多姿态人脸识别的人脸姿态矫正算法,在一定程度上提高了大姿态下人脸识别的准确率。方法 对传统的3维形变模型拟合方法进行改进,利用人脸形状参数和表情参数对3维形变模型进行建模,针对面部不同区域的关键点赋予不同的权值,加权拟合3维形变模型,使得具有不同姿态和面部表情的人脸图像拟合效果更好。然后,对3维人脸模型进行姿态矫正并利用深度学习对人脸图像进行修复,修复不规则的人脸空洞区域,并使用最新的局部卷积技术同时在新的数据集上重新训练卷积神经网络,使得网络参数达到最优。结果 在LFW（labeled faces in the wild）人脸数据库和StirlingESRC（Economic Social Research Council）3维人脸数据库上,将本文算法与其他方法进行比较,实验结果表明,本文算法的人脸识别精度有一定程度的提高。在LFW数据库上,通过对具有任意姿态的人脸图像进行姿态矫正和修复后,本文方法达到了96.57%的人脸识别精确度。在StirlingESRC数据库上,本文方法在人脸姿态为±22°的情况下,人脸识别准确率分别提高5.195%和2.265%;在人脸姿态为±45°情况下,人脸识别准确率分别提高5.875%和11.095%;平均人脸识别率分别提高5.53%和7.13%。对比实验结果表明,本文提出的人脸姿态矫正算法有效提高了人脸识别的准确率。结论 本文提出的人脸姿态矫正算法,综合了3维形变模型和深度学习模型的优点,在各个人脸姿态角度下,均能使人脸识别准确率在一定程度上有所提高。     

HOG和多流形判别分析相融合的人脸识别

为了提高人脸的识别率,提出一种方向梯度直方图和多流形判别分析相融合的人脸识别算法。将一幅图像划分为多个子块图像块,并采用方向梯度直方图算法对每一个小块进行处理;采用多流形判别分析提取特征,并采用最小二乘支持向量机建立分类器对人脸进行分类和识别;在Yale和AR人脸库进行仿真实验。实验结果表明,相对于传统人脸别算法,该算法不仅提高了人脸识别率和识别速度,并且对光照和姿态变化具有较强的鲁棒性。     

基于少量特征点的多模态人脸识别

针对二维人脸识别对姿态和光照变化较为敏感的问题,提出了一种基于少量特征点的多模态人脸识别方法。在训练阶段,对三维人脸数据进行二次处理和数据挖掘,为建立完备的特征模板奠定了基础;提出了一种简洁高效的样本聚类方法,克服了特征提取过程中的非线性问题;通过局部特征分析(Local Feature Analysis,LFA)实现了特征点"局部"与"全局"信息的融合。实验证明该方法在具有较高执行效率的同时,对人脸图像的姿态和光照变化具有理想的鲁棒性,在WHU-3D小规模人脸数据库上取得了98.06%的识别率。     

基于低秩表示中稀疏误差的可变光照和局部遮挡人脸识别

可变光照和有遮挡人脸识别是人脸识别问题中的一个难点。受到鲁棒主成分分析法(RPCA)和稀疏表示分类法(SRC)的启发,提出一种基于低秩表示(LRR)中稀疏误差图像的可变光照有遮挡人脸识别算法。在训练阶段,利用LRR计算每类人脸低秩数据矩阵,在此基础上求解每类人脸图像低秩映射矩阵,通过各类低秩映射矩阵将未知人脸图像投影得到每类下的低秩数据矩阵和稀疏误差矩阵,为了有效提取稀疏误差图像中的鉴别信息,分别对稀疏误差图像进行边缘检测和平滑度分析,设计了基于两者加权和的类别判据。在Extended Yale B和AR两个数据库上进行了详细的实验分析,实验结果与其它算法相比较有明显提高,证实了所提算法的有效性和鲁棒性。     

基于曲波变换和余弦测度的人脸识别方法

人脸识别是一种常用的生物特征识别技术,广泛应用于门禁考勤、公安司法等领域。光照、人脸表情与姿态、遮挡等采集条件的变化对 现有人脸识别方法 影响较大,限制了其应用。提出了一种基于曲波变换和余弦测度的人脸识别方法,以提高人脸识别对采集条件的鲁棒性。首先,对待识别人脸图像进行曲波变换,依据曲波系数检测人脸区域的关键点;然后,提取各关键点在不同尺度和方向上的曲波特征,构建人脸特征描述子;最后,依据余弦测度、累加和运算和极值运算求取人脸的最优匹配结果。仿真实验表明,所提方法对光照、姿态、表情和遮挡等变化的鲁棒性强,且识别性能好。     

基于视频的人脸识别研究进展

近年来基于视频的人脸识别已成为人脸识别领域最为活跃的研究方向之一.如何充分利用视频中人脸的时间和空间信息克服视频中人脸分辨率低,尺度变化范围大,光照、姿态变化比较剧烈以及时常发生遮挡等困难是研究的重点.文中对近期(主要近5年)基于视频的人脸识别研究进行了详细的介绍和讨论,在对相关方法分类的基础上,分析了各类方法中典型技术的优缺点,并概况介绍了常用的视频人脸数据库和实验结果,最后展望了基于视频人脸识别未来的发展方向和趋势.     

面向大姿态人脸识别的正面化形变场学习

目的 人脸识别已经得到了广泛应用,但大姿态人脸识别问题仍未完美解决。已有方法或提取姿态鲁棒特征,或进行人脸姿态的正面化。其中主流的人脸正面化方法包括2D回归生成和3D模型形变建模,前者能够生成相对自然真实的人脸,但会引入额外的噪声导致图像信息的扭曲;后者能够保持原始的人脸结构信息,但生成过程是基于物理模型的,不够自然灵活。为此,结合2D和3D方法的优势,本文提出了基于由粗到细形变场的人脸正面化方法。方法 该形变场由深度网络以2D回归方式学得,反映的是不同视角人脸图像像素之间的语义级对应关系,可以类3D的方式实现非正面人脸图像的正面化,因此该方法兼具了2D正面化方法的灵活性与3D正面化方法的保真性,且借鉴分步渐进的思路,本文提出了由粗到细的形变场学习框架,以获得更加准确鲁棒的形变场。结果 本文采用大姿态人脸识别实验来验证本文方法的有效性,在MultiPIE(multi pose, illumination, expressions)、LFW(labeled faces in the wild)、CFP(celebrities in frontal-profile in the wild)...     

相关滤波器和2DPCA相融合的变光照人脸识别

为解决变光照下人脸识别的识别率低问题,提出一种最佳相关滤波和2DPCA相融合的光照人脸识别方法。通过采用特定类2DPCA重构人脸图像,生成一对相关滤波器;测试人脸图像通过相关性滤器将投影到二维子空间中,并根据预先设定的峰旁瓣比阈值进行人脸识别;最后采用PIE和YaleB人脸库进行仿真实验。相比其他人脸识别方法,该方法获得了更高的人脸识别率,鲁棒性更强。     

基于随机投影与加权稀疏表示残差的光照鲁棒人脸识别方法

针对人脸识别中的光照变化问题,利用随机投影对传统稀疏表示分类器进行改进,提出一种基于随机投影与加权稀疏表示残差的光照鲁棒人脸识别方法。通过对人脸图像进行光照规范化处理,尽量消除人脸图像上的恶劣光照,取得经光照校正的人脸样本后进行多次随机空间投影,进一步丰富样本的光照不变特征,以减小光照变化对人脸识别带来的影响。在此基础上,对利用单一残差分类的传统稀疏表示分类方法进行改进,样本经过多次随机投影和稀疏表示会产生多个样本特征和重构残差,利用样本特征的能量来确定各个重构残差的融合权值,最终得到一种稳定性和可靠性更强的加权残差。在 Yale B 和 CMU PIE 两个光照变化较大的人脸库上的实验结果表明,改进的方法具有较强的光照鲁棒性。与传统稀疏表示方法相比,本文提出的方法在Yale B人脸库上两组实验的平均识别率分别提高了25.76%和46.39%,在CMU PIE上的平均识别率提高了10%左右。     

Pose-and-illumination-invariant face representation via a triplet-loss trained deep reconstruction model

Face recognition under variable pose and illumination is a challenging problem in computer vision tasks. In this paper, we solve this problem by proposing a new residual based deep face reconstruction neural network to extract discriminative pose-and-illumination-invariant (PII) features. Our deep model can change arbitrary pose and illumination face images to the frontal view with standard illumination. We propose a new triplet-loss training method instead of Euclidean loss to optimize our model, which has two advantages: a) The training triplets can be easily augmented by freely choosing combinations of labeled face images, in this way, overfitting can be avoided; b) The triplet-loss training makes the PII features more discriminative even when training samples have similar appearance. By using our PII features, we achieve 83.8% average recognition accuracy on MultiPIE face dataset which is competitive to the state-of-the-art face recognition methods.     

一种基于块共同特征值的人脸识别方法

主成分分析与线性判别分析是人脸识别的重要识别方法,它们都通过求解特征值问题实现特征提取,但由于维数灾难会导致小样本和奇异性问题。提出了一种简单的人脸识别方法,无需进行奇异值分解,能有效地降低计算代价。首先将图像划分成块,然后计算多项式系数,得到友阵用于特征提取。基于两张不同图像的多项式系数友阵来计算对称阵。最后通过计算对称阵的零空间的零化度识别相似的人脸图像。为验证提出方法的有效性,在ORL、Yale和FERET人脸数据库上进行了实验。结果表明,该方法对于有较大姿态与光照变化的人脸识别具有较高的识别性能。     

Virtual illumination grid for correction of uncontrolled illumination in facial images

Face recognition under uncontrolled illumination conditions is still considered an unsolved problem. In order to correct for these illumination conditions, we propose a virtual illumination grid (VIG) approach to model the unknown illumination conditions. Furthermore, we use coupled subspace models of both the facial surface and albedo to estimate the face shape. In order to obtain a representation of the face under frontal illumination, we relight the estimated face shape. We show that the frontal illuminated facial images achieve better performance in face recognition. We have performed the challenging Experiment 4 of the FRGCv2 database, which compares uncontrolled probe images to controlled gallery images. Our illumination correction method results in considerably better recognition rates for a number of well-known face recognition methods. By fusing our global illumination correction method with a local illumination correction method, further improvements are achieved.     

基于性别和年龄因子分析的鲁棒性人脸表情识别

针对非可控环境下人脸表情识别面临的诸如种族、性别和年龄等因子变化问题, 提出一种基于深度条件随机森林的鲁棒性人脸表情识别方法.与传统的单任务人脸表情识别方法不同, 设计了一种以人脸表情识别为主, 人脸性别和年龄属性识别为辅的多任务识别模型.在研究中发现, 人脸性别和年龄等属性对人脸表情识别有一定的影响, 为了捕获它们之间的关系, 提出一种基于人脸性别和年龄双属性的深度条件随机森林人脸表情识别方法.在特征提取阶段, 采用多示例注意力机制进行人脸特征提取以便去除诸如光照、遮挡和低分辨率等变化问题; 在人脸表情识别阶段, 根据人脸性别和年龄双属性因子, 采用多条件随机森林方法进行人脸表情识别.在公开的CK+, ExpW, RAF-DB, AffectNet人脸表情数据库上进行了大量实验：在经典的CK+人脸库上达到99%识别率, 在具有挑战性的自然场景库(ExpW, RAF-DB, AffectNet组合库)上达到70.52%的识别率.实验结果表明：与其他方法相比具有先进性, 对自然场景中的遮挡、噪声和分辨率变化具有一定的鲁棒性.