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并进行维数约简,但它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,而且流形学习方法大多没有明晰的投影矩阵,很难直接对新样本进行维数约简。针对这两个问题...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并进行维数约简,但它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,而且流形学习方法大多没有明晰的投影矩阵,很难直接对新样本进行维数约简。针对这两个问题,提出一种新的有监督的核局部线性嵌入算法(SKLLE,supervised ker-nel local linear embedding)。该算法通过非线性核映射将人脸样本投影到高维核特征空间,然后将人脸局部流形的结构信息和样本的类别信息进行有效地结合进行维数约简,提取低维鉴别流形特征用于分类。SKLLE算法不仅能发现嵌入于高维人脸图像的低维子流形,而且增强了局部类间的联系,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性别识别的性能。展开更多
In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algori...In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.展开更多
文摘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并进行维数约简,但它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,而且流形学习方法大多没有明晰的投影矩阵,很难直接对新样本进行维数约简。针对这两个问题,提出一种新的有监督的核局部线性嵌入算法(SKLLE,supervised ker-nel local linear embedding)。该算法通过非线性核映射将人脸样本投影到高维核特征空间,然后将人脸局部流形的结构信息和样本的类别信息进行有效地结合进行维数约简,提取低维鉴别流形特征用于分类。SKLLE算法不仅能发现嵌入于高维人脸图像的低维子流形,而且增强了局部类间的联系,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性别识别的性能。
文摘In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.
