基于划分的有监督局部切空间排列的人脸识别*

为了解决流形学习不能充分利用样本类别信息的问题,提出了一种基于划分的有监督局部切空间排列算法,并将其应用于人脸识别。新算法采用基于动态粒子群算法的有监督的K-均值聚类算法确定样本的聚类中心,将样本划分为有重叠的块。新算法在利用数据类别信息的同时保持了流形的局部几何结构,提高了流形学习对图像的识别能力,能更好的适用于人脸识别。通过在ORL数据库上与其他流形方法比较,验证了新算法的有效性。     

基于Uniform LBP和DMMA的单样本人脸识别

在单样本人脸识别系统中,为了获得更好的人脸面部特征,提出了一种融合Uniform LBP特征和多流形判别分析（Discriminative Multi-Manifold Analysis,DMMA）的特征提取方法。对每幅人脸图像进行分块构成一个子集。使用统一局部二值模式（Uniform LBP）算子提取每个子集中图像的直方图,每个子集中的直方图形成一个统计流形,应用DMMA算法获得人脸图像的低维特征。采用基于重建的流形-流形间的距离识别未知的人脸图像。在AR数据库和ORL数据库上实验结果表明,该算法的识别性能优于一般的DMMA算法。     

基于核融合的多信息流形学习算法*

流形学习算法可分为全局流形学习与局部流形学习,它们分别保持了流形上的全局特征信息与局部特征信息。但是实验证明仅基于单一特征信息的流形学习算法不能很好的保持真实的流形结构,影响了学习效果。因此,基于流形学习的核的视角,将全局流形学习算法ISOMAP与局部流形学习算法LTSA的核进行融合,提出了可以同时保持流形结构的全局特征信息与局部特征信息的流形学习算法,在人工数据集和人脸图像集上的仿真实验证明了本文算法的有效性。     

黎曼流形上的保局投影在图像集匹配中的应用

目的提出了黎曼流形上局部结构特征保持的图像集匹配方法。方法该方法使用协方差矩阵建模图像集合,利用对称正定的非奇异协方差矩阵构成黎曼流形上的子空间,将图像集的匹配转化为流形上的点的匹配问题。通过基于协方差矩阵度量学习的核函数将黎曼流形上的协方差矩阵映射到欧几里德空间。不同于其他方法黎曼流形上的鉴别分析方法,考虑到样本分布的局部几何结构,引入了黎曼流形上局部保持的图像集鉴别分析方法,保持样本分布的局部邻域结构的同时提升样本的可分性。结果在基于图像集合的对象识别任务上测试了本文算法,在ETH80和YouTube Celebrities数据库分别进行了对象识别和人脸识别实验,分别达到91.5%和65.31%的识别率。结论实验结果表明,该方法取得了优于其他图像集匹配算法的效果。     

基于DCT域内拉普拉斯值排序的人脸识别方法

基于DCT域内的人脸识别方法的关键是如何选择有效的DCT系数,提出了一种基于DCT域内拉普拉斯值排序的人脸识别方法。首先将图像划分为若干个大小相同的子块,对每一个子块进行DCT变换,得到分块DCT系数,然后利用拉普拉斯值作为局部保持能力判据选择那些能够很好保持样本流形结构的分块DCT系数,最后对选定的DCT系数执行LPP算法提取识别特征,在ORL和Yale人脸数据库上的实验结果证明了该方法的有效性。     

基于虚拟样本图像集的多流形鉴别学习算法

为了丰富训练样本的类内变化信息,提出了基于通用训练样本集的虚拟样本生成方法。进一步,为了利用生成的虚拟样本中的类内变化信息有效地完成单样本人脸识别任务,提出了基于虚拟样本图像集的多流行鉴别学习算法。该算法首先将每类仅有的单个训练样本图像和该类的虚拟样本图像划分为互补重叠的局部块并构建流形,然后为每个流形学习一个投影矩阵,使得相同流形内的局部块在投影后的低维特征空间间隔最小化,不同流形中的局部块在投影后的低维特征空间中间隔最大化。实验结果表明,所提算法能够准确地预测测试样本中的类内变化,是一种有效的单样本人脸识别算法。     

考虑局部邻域多流形度量的单训练样本人脸识别

针对单训练样本中识别干扰因素较多的问题,为了增加人脸识别效果,在考虑局部邻域多流形度量的基础上,提出一种新的单训练样本人脸识别算法。首先预处理人脸图像信息,将影响因素降到最低;划分数据集,根据划分结果对分布在流形结构内数据点计算近邻点;构建多流形距离度量矩阵和误差度量矩阵;将人脸图像经过投影降维转换为低维流形结构,完成单训练样本人脸识别。实验结果验证了所设计算法识别效率较高和平均识别率较高,所用时间较少,具有很好的优势。     

基于图像分块和特征选择的单训练样本人脸识别

为了提高人脸识别的正确率,针对单样本人脸识别训练样本存在的缺陷,提出一种基于图像分块和特征选择的单样本人脸识别算法。首先将人脸图像划分成子块,并分别提取各子块的特征,连接成人脸图像特征向量,然后采用多流形判断分析算法选择对人脸识别结果贡献较大的特征。最后计算采用支持向量机对人脸进行识别,并采用Yale B和PIE人脸库对本文人脸算法的有效性和优越性进行仿真测试。仿真结果表明,相对于当前典型人脸识别算法,该算法提高了人脸识别正确率,获得了更加理想的人脸识别效果。     

标架丛上的局部特征联络学习算法

人脸识别问题中,经常会面临样本少的情况,在身份证识别、电子护照识别等系统中,甚至只有一个训练样本,很多传统人脸识别方法在处理单样本时将失效。从流形学习角度出发提出了一种有效解决单样本人脸识别的方法。以自组织映射神经网络为基础,将人脸局部特征(眼、鼻、嘴等)视为一个流形,训练出多流形结构。利用联络关联不同的流形,同时学习出局部特征流形间与流形内的方向变化信息,再进行有监督的训练。整个方法结合了神经网络学习和流形学习,将单样本人脸识别问题转换成多流形匹配问题。在著名人脸库ORL、UMIST、FERET、AR上的实验显示该算法在处理单样本问题时优于已有算法,在处理姿态、表情等变化问题时也表现出很好的效果。     

融合LBP和表观流形鉴别分析的人脸识别算法

流形学习方法可以有效地发现存在于高维图像空间的低维子流形,但是流形学习是一种非监督学习方法,其鉴别能力反而不如传统的维数约简方法,且对人脸图像的光照、姿态等局部变化敏感,针对这两个问题,本文提出一种基于人脸表观流形鉴别分析的识别方法,该方法利用局部二元模式(Local binary pattern,LBP)对人脸图像进行局部特征描述,提取对局部变化不敏感的特征,然后使用有监督的核局部线性嵌入算法(Supervised kernell ocal linear embedding,SKLLE)对由局部特征构造的全局特征进行维数约简,提取低维鉴别流形特征进行人脸识别,该方法不仅对局部变化不敏感,而且将人脸表观流形和类别信息进行有效的结合,同时对新样本有较好的泛化性,实验结果表明该算法能有效的提高人脸识别的性能.     

A structure-preserved local matching approach for face recognition

In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.     

An adaptively weighted sub-pattern locality preserving projection for face recognition

In this paper, an adaptively weighted sub-pattern locality preserving projection (Aw-SpLPP) algorithm is proposed for face recognition. Unlike the traditional LPP algorithm which operates directly on the whole face image patterns and obtains a global face features that best detects the essential face manifold structure, the proposed Aw-SpLPP method operates on sub-patterns partitioned from an original whole face image and separately extracts corresponding local sub-features from them. Furthermore, the contribution of each sub-pattern can be adaptively computed by Aw-SpLPP in order to enhance the robustness to facial pose, expression and illumination variations. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, YaleB and PIE). Experimental results show that Aw-SpLPP outperforms other holistic and sub-pattern based methods.     

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。     

A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition

Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA I) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods.     

基于子模式行列方向二维线性判别分析特征融合的特征提取

针对人脸识别中表情和光照变化引起的面部变化、灰度不均匀等识别问题,提出一种基于子模式行列方向二维线性判别分析(Sp-RC2DLDA)的特征提取方法。该方法通过对原图像进行子模式分块处理,能有效提取图像的局部特征,减少表情、光照变化的影响,通过把相同位置的子图像组成子样本集,合理利用了子块间的空间关系,进一步提高了识别率;同时,对各个子样本集分别利用行方向二维线性判别分析(2DLDA)和列方向扩展2DLDA(E2DLDA)进行特征抽取,得到互补的行、列方向子图像特征,并分别把子图像特征组合成原图像的特征矩阵,然后利用一种特征融合方法对行、列方向特征矩阵进行有效融合,对互补的特征空间进行融合有效地改善了识别性能;最后采用最近邻分类器进行人脸识别实验。在Yale及ORL人脸库上的实验结果表明,Sp-RC2DLDA有效地减少了表情和光照变化的影响,具有较好的鲁棒性。     

典型相关分析融合全局和局部特征的人脸识别

为了提高人脸的识别率,提出一种典型相关分析融合全局和局部特征的人脸识别算法（SUB-CCA）。通过划分子模式方式避免人脸识别存在小样本、非线性问题,并提取局部特征,采用主成分分析提取人脸图像的全局特征,并采用相关分析算法对全局、局总特征进行融合,消除特征间冗余信息,降低特征维数,采用投票法得到人脸识别结果,并采用3个人脸数据集对算法性能进行测试。仿真结果表明,相对于参比算法,SUB-CCA提高了人脸识别的识别精度。     

基于分块相位一致性的人脸识别算法

针对基于可见光的人脸图像的识别容易受光照和表情变化的影响,人脸的表情变化仅限于局部等问题,以及图像的相位一致性特征不受图像的亮度或对比度影响的特点,提出了一种基于分块相位一致性的人脸识别算法。该算法用log-gabor滤波器对图像进行滤波,利用相位一致性模型提取相位一致性特征图像;对每幅特征图像进行分块主元分析(PCA)处理;融合所有子图像的距离信息,采用最近邻分类器进行分类识别。实验证明该方法具有更好的识别性能。     

Face recognition using elastic local reconstruction based on a single face image

In this paper, we propose a new face recognition algorithm based on a single frontal-view image for each face subject, which considers the effect of the face manifold structure. To compare two near-frontal face images, each face is considered a combination of a sequence of local image blocks. Each of the image blocks of one image can be reconstructed according to the corresponding local image block of the other face image. Then an elastic local reconstruction (ELR) method is proposed to measure the similarities between the image block pairs in order to measure the difference between the two face images. Our algorithm not only benefits from the face manifold structure, in terms of being robust to various image variations, but also is computationally simple because there is no need to build the face manifold. We evaluate the performance of our proposed face recognition algorithm with the use of different databases, which are produced under various conditions, e.g. lightings, expressions, perspectives, with/without glasses and occlusions. Consistent and promising experimental results were obtained, which show that our algorithm can greatly improve the recognition rates under all the different conditions.     

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.