【摘要】 标准的LDA方法通常有3个问题:1)为了确保类内散度矩阵的非奇异性,必须首先通过PCA进行维数约简,这限制了对更多维数空间的使用;2)当每人只有单个训练样本时,类内散度矩阵必然奇异,此时LDA无法工作;3)缺乏对像素间的局部相关性的考虑。为了解决这些问题,提出一种基于拉普拉斯方向的差值线性判别分析方法。该方法通过拉普拉斯方向实现更鲁棒的图像相异性测度,通过引入差值散度矩阵来避免类内散度矩阵的奇异性。实验结果显示,该算法对表情变化、光照改变及不同遮挡情况获得了更高的识别率,尤其针对光照变化,效果更加显著。更多还原

【Abstract】 For the traditional linear discriminant analysis method,there are usually three questions:1)In order to ensure that the within-class scatter matrix is nonsingular,the principal component analysis must firstly is performed,which limits the effect of multidimensional space.2)If the number of training samples per person is single,the within-class scatter matrix is generally singular,and the method does not work.3)Without considering the partial correlation between pixels.To address these problems,this paper proposed a different linear discriminant analysis based on Laplacian orientations.The usage of the Laplacian orientations results in a more robust dissimilarity measures between images. The introduction of the difference scatter matrix avoids the singularity of the within-class scatter matrix.Experiments show that the proposed method has better robustness for facial expressions,illumination changes and different occlusions,and achieves a higher recognition rate.Especially for illumination changes,the effect is better.更多还原