在人脸识别算法中，已有的计算不相关鉴别矢量集的算法均是基于图像向量模型的，因而将遇到所谓的小样本问题，而且由于采用迭代求解方式，算法运算速度缓慢，为此提出了一种新的求取不相关鉴别矢量集的算法，即一种基于图像矩阵模型的2维不相关鉴别矢量集算法。算法由于采用了图像矩阵模型，解决了小样本问题，通过对类内散布矩阵的白化变换，使得推广的2维线性鉴别分析模型具有类似的2维主成分分析模型的形式，从而将两种算法的模型有效地联系起来，进而可以非迭代地求得2维不相关鉴别矢量集，不但求解速度快且数值解稳定。在ORL和Yale人脸库上的实验结果表明，该算法不但减少了计算时间，同时也提高了识别率，为求解不相关鉴别矢量集提供了一个新的思路。

All of the existed algorithms in face recognition, which can obtain uncorrelated discriminant vectors, are based on image vector model, so they encounter so called “small sample size” problem. These algorithms, which are solved using recursive methods, require much computation time. So a new algorithm is proposed in this paper, which is called Two-dimensional Uncorrelated Discriminant Vectors based on an image matrix model. The new algorithm solves small sample size problem through whitening transform of within-class scatter matrix, which makes the model of extended Two-dimensional Linear Discriminant Analysis have similar form of Two-dimensional Principal Component Analysis model. Thus two algorithms were combined effectively, uncorrelated discriminant vectors can be obtained non-recursively. The new method computes fast while maintaining numerical stability. The numerical experiments on facial databases of ORL and Yale show that the proposed method has not only reduced the computation complexity but also achieved higher recognition accuracy, providing new thought on how to obtain uncorrelated discriminant vectors.