具有统计不相关性的广义图像投影鉴别分析

提出了一种新的图像投影鉴别分析方法。首先，与Liu投影鉴别分析方法相比，具有能够消除投影特征分量之间相关性的优点。其次，该方法从整体上考虑投影集的可分性。即样本在图像最佳鉴别矢量上的投影集从整体上具有最佳的可分性。另外，所提出的方法是直接基于图像矩阵的，与以往的基于图像向量的鉴别方法相比，它的突出优点是大大地提高了特征抽取的速度。最后，在0RL标准人脸库上的试验结果表明，所提出的图像投影鉴剐分析方法较Liu的方法在识别性能上有了较大幅度的提高，在普通的分类器下达到95％识别率。该识别率明显优于颇有影响的Fisher-faces方法，其特征抽取的速度提高了近19。68倍。

Statistical Uncorrelated Generalized Image Projection Discriminant Analysis

In this paper, a novel image projection analysis method is developed for image feature extraction. First, with contrast to the Liu's projection analysis method, the proposed one has a desirable property that the projective features vectors are mutual uncorrelated. Second, our method has that the separability of the projected set of the samples is considered from global view when calculating the image optimal set of discriminant vectors,that is the projected set of the samples on the image optimal set of discriminant vectors have the best separability in global sense.Furthermore, the proposed method is directly based on image matrices. That is to say, it needs not to convert the image matrix into high dimensional image vector like the previous linear discriminant methods based on image vectors. So much computational time would be saved if using our method for feature extraction. Finally, the proposed method is tested on ORL face databases. The experimental results indicate that the proposed method outperforms Lius', and a recognition rate of 95% on ORL are achieved with ordinary classifiers. The experimental results also indicate that the proposed method is more powerful than Fisherfaces, and more importantly, its speed for feature extraction is nearly 19.68 times faster.