人脸与掌纹识别的子空间特征提取方法研究
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摘要
本文对人脸与掌纹识别的子空间特征提取方法进行了深入研究,主要包括流形学习导出的新子空间方法、基于矩阵的特征提取方法的图嵌入理论框架及其在人脸和掌纹识别中的应用。此外,本文还讨论了人脸和掌纹的子空间特征在特征提取层的融合问题。主要研究成果如下:
1.局部保持投影算法是一种最近提出的子空间方法,它考虑了样本空间的图结构,在降维过程中,能够保持样本空间的局部结构和本质几何特性。但是在实际应用中,局部保持投影算法采用主成分分析加局部保持投影两个步骤,存在不直接、非完全的问题。针对这些问题,我们提出了一种直接局部保持投影算法,该算法通过同时对角化的方法求解局部保持投影问题,避免了矩阵的奇异性。它以高维原始图像数据作为输入,直接优化局部保持投影准则,没有任何维数削减的中间步骤。在香港理工大学掌纹库和ORL人脸库上的实验证明了这种方法的有效性。
2.局部保持投影算法基于向量空间模型,在这种模型中需要把原始图像按行或列串接起来,这既破坏了图像的空间结构又导致串接后数据维数过高。受二维主成分分析算法思想的启发,我们提出了一种直接基于局部保持标准和图像矩阵投影的方法——二维局部保持投影算法。我们的算法直接处理图像矩阵,而不是处理展开的图像向量。在Yale人脸库和香港理工大学掌纹库上的实验结果证明我们的方法在识别能力上比主成分分析、基于向量的局部保持投影算法和二维主成分分析方法更加有效。
3.局部保持投影算法是一种线性方法,不能提取图像的非线性的特征。为了提取图像的非线性局部保持特征,我们提出的核局部保持投影方法首先通过核函数把样本非线性地映射到一个特征空间,在这个特征空间里,数据具有一个线性的或者尽可能线性可分的结构,然后在特征空间里实施局部保持投影算法来寻找局部保持投影向量,从而完成分类任务。核方法被用来把核局部保持投影方法变成一个在核主成分分析方法变换后的空间里执行局部保持投影算法的问题。在ORL人脸库和香港理工大学掌纹库上的实验证明了这种方法的有效性。
4.最近学术界提出的多种基于矩阵的方法已经被证明是解决基于向量方法的高维和小子样问题的有效方法。我们从图嵌入的角度出发,提出了一个基于矩阵的特征提取方法的一般理论框架。通过设计满足不同目标函数的图结构,这个框架可以用于导出新的算法,基于此我们提出了一种基于矩阵的算法——二维鉴别嵌入分析。它通过结合局部类内紧凑信息和非局部类间分离信息显式地考虑了基于矩阵的类内子流形和类间子流形。二维鉴别嵌入分析方法不需要对数据分布进行任何假设,因而是一个简单的数据驱动方法。我们证明目前的二维线性鉴别分析方法实际上是二维鉴别嵌入分析方法的一个特例。在三个公开的数据库上的实验验证了二维鉴别嵌入分析方法的有效性。
5.在实际应用中,由于环境的复杂性和不可预见性,基于单一生物特征的识别系统经常显现出一些难以克服的困难。因此,本文利用人脸和掌纹的子空间特征在特征层融合进行身份鉴别。我们对人脸和掌纹的特征提取使用两种常用的子空间方法:主成分分析方法和独立成分分析方法。实验结果发现,在两种情况下人脸和掌纹在特征层融合后,系统的性能都有了很大的提升,尤其是在使用独立成分分析方法提取特征的情况下,在40个人规模的测试集上,取得了99.17%的准确识别率。我们的结果有力地证明利用人脸和掌纹的多生物特征识别系统的性能要比单个人脸或掌纹系统识别好得多。
This thesis is focused on some issues related to subspace feature extraction methods for face and palmprint recognition. These issues mainly include novel subspace analysis methods derived from the idea of manifold learning, the theoretical framework of graph embedding for matrix-based feature extraction algorithms with their applications to face and palmprint recognition. In addition, in this thesis the fusion of the subspace features of face and palmprint at the feature extraction level is also discussed. The main contributions can be exhibited by the following aspects:
1. As an alternative subspace method, Locality Preserving Projections (LPP) algorithm has been proposed recently, which takes into account the space structure of the samples, and in the process of dimension reduction, it can thus find a good linear embedding that preserves local structural information and intrisinc geometry of the data space. However, in practice for the LPP algorithm, a two-step framework (Principal Component Analysis + Locality Preserving Projections) is required, which is indirect and uncomplete. To attack these problems, in this thesis a Direct Locality Preserving Projections (DLPP) algorithm is proposed. This algorithm solves locality preserving problem via simultaneous diagonalization, and can avoid the singularity of the matrices. This algorithm accepts high-dimensional raw images as input, and optimizes locality preserving criterion directly, without any dimensionality reduction steps. Experimental results on the PolyU palmprint database and the ORL face database show the effectiveness of the proposed algorithm.
2. The LPP algorithm is based on vector-space model. Under this model, the original two-dimensional image data are reshaped into a one-dimensional long vector by rows or columns, which leads to not only the loss of some structural information residing in original 2D images but also a too high-dimensional data space. Inspired by the idea of two-dimensional Principal Component Analysis (2DPCA) algorithm, in this thesis a novel algorithm, two-dimensional Locality Preserving Projections (2DLPP) algorithm, is proposed, which is a straightforward manner based on locality preserving criterion and the image matrix projection. This algorithm directly projects the image matrix under a specific projection criterion, rather than using the stretched image vector. Experimental results on the Yale face database and the PolyU palmprint database show that the 2DLPP algorithm outperforms the conventional Principal Component Analysis (PCA), vector-based LPP and two-dimensional Principal Component Analysis (2DPCA) algorithms in terms of the recognition accuracy rates.
3. As the LPP model is linear, it may fail to extract the nonlinear features. To attack this problem, the Kernel Locality Preserving Projections (KLPP) algorithm proposed in this thesis is to nonlinearly map the data into a feature space in which the dataset has a linear structure or a structure as linearly separable as possible, then LPP is performed in feature space to find the locality preserving projection vectors for final classification. Kernel tricks are used to change the problem of implementing KLPP algorithm in feature space into a problem of performing LPP in the Kernel Principal Component Analysis (KPCA) transformed space. Experiments on the ORL face database and the PolyU palmprint database demonstrate the effectiveness of the proposed algorithm.
4. Recently proposed matrix-based methods in the research community have been shown to be effective ways to avoid the problems of high dimensionality and small sample sizes that are associated with vector-based methods. In this thesis, a general framework for matrix-based feature extraction algorithms is proposed from the point of view of graph embedding. It is found that through designing meaningful graph structures which satisfy various objective functions, this framework can be used as a platform to derive new matrix-based algorithms, in this direction, a novel matrix-based algorithm, i.e. two-dimensional Discriminant Embedding Analysis (2DDEA). is proposed, which explicitly takes into account the matrix-based intra-class submanifold and inter-class submanifold by integrating the local intra-class compactness information and the non-local inter-class separability information. The proposed algorithm does not require any hypotheses on the distribution of the dataset and is thus a simple data-driven approach. It is also shown that 2DLDA is actually a special case of the proposed 2DDEA method. Experimental results on three public image databases show the effectiveness of the proposed algorithm.
5. In practical applications, due to the comlexity and the unpredictability of the working environments, biometric systems based on single trait have exhibited several unresolved problems. Therefore, in this thesis the subspace features of face and palmprint are fused at the feature extraction level for personal identification. Two well developed subspace methods, i.e. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used to extract the face and palmprint features. It is found that the performance is significantly improved in both cases, especially in the case of feature fusion using ICA, encouraging results with a 99.17% recognition accuracy rate using a test set sized of 40 people are obtained. The results of this work suggest that a multimodal system integrating of faces and palmprints can offer substantial performance gain that may not be possible with a single biometric indicator alone.
1.局部保持投影算法是一种最近提出的子空间方法,它考虑了样本空间的图结构,在降维过程中,能够保持样本空间的局部结构和本质几何特性。但是在实际应用中,局部保持投影算法采用主成分分析加局部保持投影两个步骤,存在不直接、非完全的问题。针对这些问题,我们提出了一种直接局部保持投影算法,该算法通过同时对角化的方法求解局部保持投影问题,避免了矩阵的奇异性。它以高维原始图像数据作为输入,直接优化局部保持投影准则,没有任何维数削减的中间步骤。在香港理工大学掌纹库和ORL人脸库上的实验证明了这种方法的有效性。
2.局部保持投影算法基于向量空间模型,在这种模型中需要把原始图像按行或列串接起来,这既破坏了图像的空间结构又导致串接后数据维数过高。受二维主成分分析算法思想的启发,我们提出了一种直接基于局部保持标准和图像矩阵投影的方法——二维局部保持投影算法。我们的算法直接处理图像矩阵,而不是处理展开的图像向量。在Yale人脸库和香港理工大学掌纹库上的实验结果证明我们的方法在识别能力上比主成分分析、基于向量的局部保持投影算法和二维主成分分析方法更加有效。
3.局部保持投影算法是一种线性方法,不能提取图像的非线性的特征。为了提取图像的非线性局部保持特征,我们提出的核局部保持投影方法首先通过核函数把样本非线性地映射到一个特征空间,在这个特征空间里,数据具有一个线性的或者尽可能线性可分的结构,然后在特征空间里实施局部保持投影算法来寻找局部保持投影向量,从而完成分类任务。核方法被用来把核局部保持投影方法变成一个在核主成分分析方法变换后的空间里执行局部保持投影算法的问题。在ORL人脸库和香港理工大学掌纹库上的实验证明了这种方法的有效性。
4.最近学术界提出的多种基于矩阵的方法已经被证明是解决基于向量方法的高维和小子样问题的有效方法。我们从图嵌入的角度出发,提出了一个基于矩阵的特征提取方法的一般理论框架。通过设计满足不同目标函数的图结构,这个框架可以用于导出新的算法,基于此我们提出了一种基于矩阵的算法——二维鉴别嵌入分析。它通过结合局部类内紧凑信息和非局部类间分离信息显式地考虑了基于矩阵的类内子流形和类间子流形。二维鉴别嵌入分析方法不需要对数据分布进行任何假设,因而是一个简单的数据驱动方法。我们证明目前的二维线性鉴别分析方法实际上是二维鉴别嵌入分析方法的一个特例。在三个公开的数据库上的实验验证了二维鉴别嵌入分析方法的有效性。
5.在实际应用中,由于环境的复杂性和不可预见性,基于单一生物特征的识别系统经常显现出一些难以克服的困难。因此,本文利用人脸和掌纹的子空间特征在特征层融合进行身份鉴别。我们对人脸和掌纹的特征提取使用两种常用的子空间方法:主成分分析方法和独立成分分析方法。实验结果发现,在两种情况下人脸和掌纹在特征层融合后,系统的性能都有了很大的提升,尤其是在使用独立成分分析方法提取特征的情况下,在40个人规模的测试集上,取得了99.17%的准确识别率。我们的结果有力地证明利用人脸和掌纹的多生物特征识别系统的性能要比单个人脸或掌纹系统识别好得多。
This thesis is focused on some issues related to subspace feature extraction methods for face and palmprint recognition. These issues mainly include novel subspace analysis methods derived from the idea of manifold learning, the theoretical framework of graph embedding for matrix-based feature extraction algorithms with their applications to face and palmprint recognition. In addition, in this thesis the fusion of the subspace features of face and palmprint at the feature extraction level is also discussed. The main contributions can be exhibited by the following aspects:
1. As an alternative subspace method, Locality Preserving Projections (LPP) algorithm has been proposed recently, which takes into account the space structure of the samples, and in the process of dimension reduction, it can thus find a good linear embedding that preserves local structural information and intrisinc geometry of the data space. However, in practice for the LPP algorithm, a two-step framework (Principal Component Analysis + Locality Preserving Projections) is required, which is indirect and uncomplete. To attack these problems, in this thesis a Direct Locality Preserving Projections (DLPP) algorithm is proposed. This algorithm solves locality preserving problem via simultaneous diagonalization, and can avoid the singularity of the matrices. This algorithm accepts high-dimensional raw images as input, and optimizes locality preserving criterion directly, without any dimensionality reduction steps. Experimental results on the PolyU palmprint database and the ORL face database show the effectiveness of the proposed algorithm.
2. The LPP algorithm is based on vector-space model. Under this model, the original two-dimensional image data are reshaped into a one-dimensional long vector by rows or columns, which leads to not only the loss of some structural information residing in original 2D images but also a too high-dimensional data space. Inspired by the idea of two-dimensional Principal Component Analysis (2DPCA) algorithm, in this thesis a novel algorithm, two-dimensional Locality Preserving Projections (2DLPP) algorithm, is proposed, which is a straightforward manner based on locality preserving criterion and the image matrix projection. This algorithm directly projects the image matrix under a specific projection criterion, rather than using the stretched image vector. Experimental results on the Yale face database and the PolyU palmprint database show that the 2DLPP algorithm outperforms the conventional Principal Component Analysis (PCA), vector-based LPP and two-dimensional Principal Component Analysis (2DPCA) algorithms in terms of the recognition accuracy rates.
3. As the LPP model is linear, it may fail to extract the nonlinear features. To attack this problem, the Kernel Locality Preserving Projections (KLPP) algorithm proposed in this thesis is to nonlinearly map the data into a feature space in which the dataset has a linear structure or a structure as linearly separable as possible, then LPP is performed in feature space to find the locality preserving projection vectors for final classification. Kernel tricks are used to change the problem of implementing KLPP algorithm in feature space into a problem of performing LPP in the Kernel Principal Component Analysis (KPCA) transformed space. Experiments on the ORL face database and the PolyU palmprint database demonstrate the effectiveness of the proposed algorithm.
4. Recently proposed matrix-based methods in the research community have been shown to be effective ways to avoid the problems of high dimensionality and small sample sizes that are associated with vector-based methods. In this thesis, a general framework for matrix-based feature extraction algorithms is proposed from the point of view of graph embedding. It is found that through designing meaningful graph structures which satisfy various objective functions, this framework can be used as a platform to derive new matrix-based algorithms, in this direction, a novel matrix-based algorithm, i.e. two-dimensional Discriminant Embedding Analysis (2DDEA). is proposed, which explicitly takes into account the matrix-based intra-class submanifold and inter-class submanifold by integrating the local intra-class compactness information and the non-local inter-class separability information. The proposed algorithm does not require any hypotheses on the distribution of the dataset and is thus a simple data-driven approach. It is also shown that 2DLDA is actually a special case of the proposed 2DDEA method. Experimental results on three public image databases show the effectiveness of the proposed algorithm.
5. In practical applications, due to the comlexity and the unpredictability of the working environments, biometric systems based on single trait have exhibited several unresolved problems. Therefore, in this thesis the subspace features of face and palmprint are fused at the feature extraction level for personal identification. Two well developed subspace methods, i.e. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used to extract the face and palmprint features. It is found that the performance is significantly improved in both cases, especially in the case of feature fusion using ICA, encouraging results with a 99.17% recognition accuracy rate using a test set sized of 40 people are obtained. The results of this work suggest that a multimodal system integrating of faces and palmprints can offer substantial performance gain that may not be possible with a single biometric indicator alone.
引文
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