提出一种多特征描述及局部决策融合的人脸识别方法。首先利用独立成分分析算法构造全局互补子空间,对待测样本进行粗分类。然后利用三种不同定义的纹理描述算法构造局部互补子空间，获取粗分类难识别样本的后验概率值，最后依据其大小设置等级分数，得到待测样本在局部互补子空间上的精确分类。在ORL、Yale 和FERET 人脸库上的实验结果表明，本文方法能较好的描述图像特征且具有较高的识别率和较低的时间复杂度，与其他方法对比也表明了本文方法的有效性。

A face recognition method is proposed based on multi features description and local fusion decision. Firstly, we use Independent Component Analysis (ICA) to construct the global complementary subspace to roughly classify the test samples. Then the texture descriptor algorithms under three different definitions are used to construct local complementary subspace to obtain the posterior probability of sample which is difficult to classify by rough classification. Finally, we get the precise classification result of test sample on the local complementary subspace through setting grade scores based on the value of the posterior probability. The experimental results on ORL, Yale and FERET face database show that the proposed method better describes characteristics of the image and has lower time complexity and higher recognition rate. Compared with other methods, it also proves its effectiveness on the face recognition.