Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disad...Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.展开更多
Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension. It has been used widely in many applications involving high-dimensional data, such as face recognition, image retrieval, ...Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension. It has been used widely in many applications involving high-dimensional data, such as face recognition, image retrieval, etc. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. A well-known approach to deal with the singularity problem is to apply an intermediate dimension reduction stage using Principal Component Analysis (PCA) before LDA. The algorithm, called PCA + LDA, is used widely in face recognition. However, PCA + LDA have high costs in time and space, due to the need for an eigen-decomposition involving the scatter matrices. Also, Two Dimensional Linear Discriminant Analysis (2DLDA) implicitly overcomes the singular- ity problem, while achieving efficiency. The difference between 2DLDA and classical LDA lies in the model for data representation. Classical LDA works with vectorized representation of data, while the 2DLDA algorithm works with data in matrix representation. To deal with the singularity problem we propose a new technique coined as the Weighted Scatter-Difference-Based Two Dimensional Discriminant Analysis (WSD2DDA). The algorithm is applied on face recognition and compared with PCA + LDA and 2DLDA. Experiments show that WSD2DDA achieve competitive recognition accuracy, while being much more efficient.展开更多
针对图像特征提取方法提取单一特征不能很好地表示图像的问题,提出了二维线性鉴别分析和协同表示的面部识别方法.该方法首先通过二维线性鉴别分析(Two-Dimensional Linear Discriminant Analysis,2DLDA)分别对训练样本的类间散布矩阵和...针对图像特征提取方法提取单一特征不能很好地表示图像的问题,提出了二维线性鉴别分析和协同表示的面部识别方法.该方法首先通过二维线性鉴别分析(Two-Dimensional Linear Discriminant Analysis,2DLDA)分别对训练样本的类间散布矩阵和类内散布矩阵提取特征,之后利用得到的特征重建图像,包括类间虚拟图像和类内虚拟图像.其次,将类间虚拟图像、类内虚拟图像和原始图像利用协同表示(Collaborative Representation,CR)算法进行得分.最后,采用加权得分融合算法将上述得分进行融合以获得最终得分,并利用最终得分进行图像识别.该方法不仅有效的抑制了光照和表情对面部识别的影响,同时根据获得的类间虚拟图像、类内虚拟图像与原始图像互补,有效的提高面部图像识别的性能.实验结果表明,该方法在不同的数据库下(ORL、AR、GT)具有较好的识别精度.展开更多
2维特征抽取方法(如2DPCA、2DLDA),因为其抽取特征的速度和识别率要比1维的方法好,所以在人脸识别中得到了广泛的应用。最近基于2DPCA又提出了对角主成份分析(diagonal principal component analysis,DiaPCA),该方法由于保持了图像的行...2维特征抽取方法(如2DPCA、2DLDA),因为其抽取特征的速度和识别率要比1维的方法好,所以在人脸识别中得到了广泛的应用。最近基于2DPCA又提出了对角主成份分析(diagonal principal component analysis,DiaPCA),该方法由于保持了图像的行变化和图像的列变化之间的相关性,从而克服了2DPCA仅能反映图像行之间的变化,而忽略了图像列之间变化的缺点。但是,由于DiaPCA并没在特征抽取中融入鉴别信息,同时2DLDA也具有与2DPCA同样的缺点,从而分别影响了DiaPCA与2DLDA两种方法的识别性能。针对这一问题,提出了一种对角线性鉴别分析(diagonal linear dicriminant analysis,DiaLDA)的新算法,该新算法是基于对角人脸图像来求解最优鉴别向量。该新算法在ORL和FERET人脸库进行了实验,并与PCA、Fisherface、DiaPCA、2DLDA等方法进行了比较。实验结果表明,该方法比其他方法的识别性能要好。展开更多
文摘Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.
文摘Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension. It has been used widely in many applications involving high-dimensional data, such as face recognition, image retrieval, etc. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. A well-known approach to deal with the singularity problem is to apply an intermediate dimension reduction stage using Principal Component Analysis (PCA) before LDA. The algorithm, called PCA + LDA, is used widely in face recognition. However, PCA + LDA have high costs in time and space, due to the need for an eigen-decomposition involving the scatter matrices. Also, Two Dimensional Linear Discriminant Analysis (2DLDA) implicitly overcomes the singular- ity problem, while achieving efficiency. The difference between 2DLDA and classical LDA lies in the model for data representation. Classical LDA works with vectorized representation of data, while the 2DLDA algorithm works with data in matrix representation. To deal with the singularity problem we propose a new technique coined as the Weighted Scatter-Difference-Based Two Dimensional Discriminant Analysis (WSD2DDA). The algorithm is applied on face recognition and compared with PCA + LDA and 2DLDA. Experiments show that WSD2DDA achieve competitive recognition accuracy, while being much more efficient.
文摘针对图像特征提取方法提取单一特征不能很好地表示图像的问题,提出了二维线性鉴别分析和协同表示的面部识别方法.该方法首先通过二维线性鉴别分析(Two-Dimensional Linear Discriminant Analysis,2DLDA)分别对训练样本的类间散布矩阵和类内散布矩阵提取特征,之后利用得到的特征重建图像,包括类间虚拟图像和类内虚拟图像.其次,将类间虚拟图像、类内虚拟图像和原始图像利用协同表示(Collaborative Representation,CR)算法进行得分.最后,采用加权得分融合算法将上述得分进行融合以获得最终得分,并利用最终得分进行图像识别.该方法不仅有效的抑制了光照和表情对面部识别的影响,同时根据获得的类间虚拟图像、类内虚拟图像与原始图像互补,有效的提高面部图像识别的性能.实验结果表明,该方法在不同的数据库下(ORL、AR、GT)具有较好的识别精度.
文摘2维特征抽取方法(如2DPCA、2DLDA),因为其抽取特征的速度和识别率要比1维的方法好,所以在人脸识别中得到了广泛的应用。最近基于2DPCA又提出了对角主成份分析(diagonal principal component analysis,DiaPCA),该方法由于保持了图像的行变化和图像的列变化之间的相关性,从而克服了2DPCA仅能反映图像行之间的变化,而忽略了图像列之间变化的缺点。但是,由于DiaPCA并没在特征抽取中融入鉴别信息,同时2DLDA也具有与2DPCA同样的缺点,从而分别影响了DiaPCA与2DLDA两种方法的识别性能。针对这一问题,提出了一种对角线性鉴别分析(diagonal linear dicriminant analysis,DiaLDA)的新算法,该新算法是基于对角人脸图像来求解最优鉴别向量。该新算法在ORL和FERET人脸库进行了实验,并与PCA、Fisherface、DiaPCA、2DLDA等方法进行了比较。实验结果表明,该方法比其他方法的识别性能要好。
