张量子空间人脸识别算法研究
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摘要
人脸识别是模式识别具体应用中的一个热点研究领域。一个完整的模式识别系统主要包括特征提取和分类器两部分。本文主要研究如何提取人脸图像中的有效分类特征以实现高效的识别。在众多的特征提取算法中,子空间算法是人脸识别中常用的特征提取方法之一。特别是近年来,张量子空间算法得到了比以往更广泛地研究。张量子空间算法和流形学习相结合成为特征提取算法的一个新的发展趋势。本文针对张量子空间算法的优点,结合其它算法的优点提出了一些新的人脸识别算法;本文也对现有的张量子空间流形学习算法中存在的不足进行了改进,提出了新的张量子空间流形学习算法。传统的子空间算法是基于向量的子空间算法,向量子空间算法有其本质的缺点,传统的张量子空间算法虽然对向量子空间算法的缺点有所改进,但没有完全克服这些缺点。为此本文引入了空间光滑子空间流形学习的思想,提出了一些新的空间光滑子空间流形学习算法。
本文的研究成果主要包括以下几个方面:
1.张量主成分分析方法相对于主成分分析方法具有更好的特征提取效果。小波变换具有良好的时频分析特性,同时还能起到降维的作用。综合利用这两个算法的优点,本文提出了一种融合小波变换和张量主成分分析的人脸识别算法。该算法首先对人脸图像先采用小波变换做预处理得到四个子带图像,然后对每个子带图像用张量主成分分析进行特征提取,最后采用最近邻方法对所提取的特征进行分类,实现了人脸图像的高效识别。
2.在结合小波和张量主成分分析进行特征提取的基础上,利用粒子群优化(PSO)算法对所提取的特征进行特征优化选择。算法过程为:首先采用小波变换和张量主成分分析方法对人脸图像进行特征提取,然后再利用PSO对提取的特征进行加权处理,根据特征的每一维元素的聚类正确率进行优化选择,从而达到对人脸提取关键性特征的目的。
3.针对现有的张量子空间流形学习算法中存在的不足,提出了一种改进算法:基于局部和全局信息的张量子空间投影。该算法充分利用人脸图像数据的局部流形结构,即数据的类内非线性流形结构,和人脸图像数据的全局信息,即数据的类别信息,使得数据在投影空间中的类间分离度最大的同时保持了原始数据的非线性流形结构。通过迭代和投影得到最优张量子空间以提高识别率。
4.根据谱图嵌入和某些流形学习算法的思想提出了一种新数据关系图矩阵确立方法,并在此基础上得出了两种利用该关系图矩阵在空间光滑的框架下求解投影矩阵进行人脸识别的算法。空间光滑约束使得两种算法比传统的张量子空间算法更加充分地考虑了图像的各像素点在图像中分布的空间相关性,同时提出的新的数据关系图矩阵确立方法确保了投影后的低维子空间特征具有最小的类内分离度和最大的类间分离度,使得投影后的低维特征有很强的分类识别能力。因此这两种算法进一步提高了识别率。
Face recognition is a task which is intensive studied in application of pattern recognition. An intact pattern recognition system consists of feature extraction and classification. The feature extraction is mainly studied in this dissertation. The subspace algorithms are among the common feature extraction algorithms. Especial in recently, the tensor subspace algorithms have been studied more extensively than before. A new trend of feature extraction algorithm is tensor subspace combined with manifold learning. The merits of tensor subspace algorithms and other algorithms are combined in my paper, so some new face recognition algorithms are put forward in my paper. Another new face recognition based tensor subspace and manifold learning is put forward based on overcoming some defects of the current tensor subspace. The face data are consider as vectors in the traditional subspace algorithms, which has its essential defects in face recognition. The essential defects have been reduced in traditional tensor subspace algorithms. But the defects in traditional tensors subspace algorithms are in existence. So the idea of spatially smooth subspace manifold learning algorithm is introduced in this dissertation. And some new face recognition algorithms based on spatially smooth subspace manifold learning are put forward in this dissertation.
In sum, the main research results achieved in this dissertation are given as follows:
1. The feature extraction effect of principal component analysis (PCA) is better than the effect of principal component analysis. And wavelet has two abilities to capture localized time-frequency information and to reduce the dimension of images. According to the two advantages of the above algorithms, a new face recognition algorithm based on wavelet transform and tensor PCA is proposed. Wavelet transform is firstly used and then tensor PCA is used to extract the feature of sub-band images, and the efficient recognition of face images can be realized.
2. The PSO and wavelet combined with tensor PCA algorithm. The extracted features which have been extracted with wavelet combined with tensor PCA algorithm are further optimized. The procedure of the algorithm is features of each face image are firstly extracted with wavelet and tensor principal component analysis algorithm. And every weights of the features’element are determined with PSO according to the clustering right rate of each element, so the object of extracting the key features of the faces can be realized.
3. For to solve the defect of the current algorithms based on tensor subspace manifold learning, a novel tensor subspace learning algorithm is proposed in this dissertation named as: tensor local and global projection. The local nonlinear structure of the data manifold that is the local information of the data can be preserved in the algorithm, at the same time the global information of data is utilized. So the discriminant between classes of data in low dimension subspace can be maximized. And the optimal tensor subspace can be obtained by iteratively computing the generalized eigenvectors and projection.
4. A new method to form affinity graph matrix of data based on spectral graph is put forward, using the method, two algorithms for face recognition under the restraining of spatially smooth are put forward. The correlation of pixels in images in the two algorithms is considered more sufficiently than in the traditional tensor subspace algorithms, at the same time, the features of the projected subspace based on new affinity graph matrix have the strongest ability of separating data between classes and the weakest ability of separating data within the same class. So the projection subspace features have stronger ability of recognition. The right recognition rates are enhanced by the two proposed algorithms, which is confirmed with experiments.
本文的研究成果主要包括以下几个方面:
1.张量主成分分析方法相对于主成分分析方法具有更好的特征提取效果。小波变换具有良好的时频分析特性,同时还能起到降维的作用。综合利用这两个算法的优点,本文提出了一种融合小波变换和张量主成分分析的人脸识别算法。该算法首先对人脸图像先采用小波变换做预处理得到四个子带图像,然后对每个子带图像用张量主成分分析进行特征提取,最后采用最近邻方法对所提取的特征进行分类,实现了人脸图像的高效识别。
2.在结合小波和张量主成分分析进行特征提取的基础上,利用粒子群优化(PSO)算法对所提取的特征进行特征优化选择。算法过程为:首先采用小波变换和张量主成分分析方法对人脸图像进行特征提取,然后再利用PSO对提取的特征进行加权处理,根据特征的每一维元素的聚类正确率进行优化选择,从而达到对人脸提取关键性特征的目的。
3.针对现有的张量子空间流形学习算法中存在的不足,提出了一种改进算法:基于局部和全局信息的张量子空间投影。该算法充分利用人脸图像数据的局部流形结构,即数据的类内非线性流形结构,和人脸图像数据的全局信息,即数据的类别信息,使得数据在投影空间中的类间分离度最大的同时保持了原始数据的非线性流形结构。通过迭代和投影得到最优张量子空间以提高识别率。
4.根据谱图嵌入和某些流形学习算法的思想提出了一种新数据关系图矩阵确立方法,并在此基础上得出了两种利用该关系图矩阵在空间光滑的框架下求解投影矩阵进行人脸识别的算法。空间光滑约束使得两种算法比传统的张量子空间算法更加充分地考虑了图像的各像素点在图像中分布的空间相关性,同时提出的新的数据关系图矩阵确立方法确保了投影后的低维子空间特征具有最小的类内分离度和最大的类间分离度,使得投影后的低维特征有很强的分类识别能力。因此这两种算法进一步提高了识别率。
Face recognition is a task which is intensive studied in application of pattern recognition. An intact pattern recognition system consists of feature extraction and classification. The feature extraction is mainly studied in this dissertation. The subspace algorithms are among the common feature extraction algorithms. Especial in recently, the tensor subspace algorithms have been studied more extensively than before. A new trend of feature extraction algorithm is tensor subspace combined with manifold learning. The merits of tensor subspace algorithms and other algorithms are combined in my paper, so some new face recognition algorithms are put forward in my paper. Another new face recognition based tensor subspace and manifold learning is put forward based on overcoming some defects of the current tensor subspace. The face data are consider as vectors in the traditional subspace algorithms, which has its essential defects in face recognition. The essential defects have been reduced in traditional tensor subspace algorithms. But the defects in traditional tensors subspace algorithms are in existence. So the idea of spatially smooth subspace manifold learning algorithm is introduced in this dissertation. And some new face recognition algorithms based on spatially smooth subspace manifold learning are put forward in this dissertation.
In sum, the main research results achieved in this dissertation are given as follows:
1. The feature extraction effect of principal component analysis (PCA) is better than the effect of principal component analysis. And wavelet has two abilities to capture localized time-frequency information and to reduce the dimension of images. According to the two advantages of the above algorithms, a new face recognition algorithm based on wavelet transform and tensor PCA is proposed. Wavelet transform is firstly used and then tensor PCA is used to extract the feature of sub-band images, and the efficient recognition of face images can be realized.
2. The PSO and wavelet combined with tensor PCA algorithm. The extracted features which have been extracted with wavelet combined with tensor PCA algorithm are further optimized. The procedure of the algorithm is features of each face image are firstly extracted with wavelet and tensor principal component analysis algorithm. And every weights of the features’element are determined with PSO according to the clustering right rate of each element, so the object of extracting the key features of the faces can be realized.
3. For to solve the defect of the current algorithms based on tensor subspace manifold learning, a novel tensor subspace learning algorithm is proposed in this dissertation named as: tensor local and global projection. The local nonlinear structure of the data manifold that is the local information of the data can be preserved in the algorithm, at the same time the global information of data is utilized. So the discriminant between classes of data in low dimension subspace can be maximized. And the optimal tensor subspace can be obtained by iteratively computing the generalized eigenvectors and projection.
4. A new method to form affinity graph matrix of data based on spectral graph is put forward, using the method, two algorithms for face recognition under the restraining of spatially smooth are put forward. The correlation of pixels in images in the two algorithms is considered more sufficiently than in the traditional tensor subspace algorithms, at the same time, the features of the projected subspace based on new affinity graph matrix have the strongest ability of separating data between classes and the weakest ability of separating data within the same class. So the projection subspace features have stronger ability of recognition. The right recognition rates are enhanced by the two proposed algorithms, which is confirmed with experiments.
引文
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