两阶段算法是指第一阶段用一个分类算法,选取距离测试样本近的M类训练样本,第二阶段再用这M类训练样本作为新的训练样本集进行识别。为了加快识别速度,提出一种全新的快速选取M类训练样本的算法。首先,利用k均值聚类算法对训练样本进行处理,把训练样本之间比较近的样本聚合成一个大类,对于一个新的测试样本,只需要计算各大类聚类中心间的距离,选取距离近的若干个大类,每个大类包含若干个原始训练样本的类别,将这些类别的所有训练样本组合起来,构成新的训练样本集,最后利用新的训练样本集,进行第二阶段的识别。在不同的人脸数据库上进行实验验证,结果表明本文算法在识别率略有提高的基础上,可以达到更快的识别速度。

In the first stage, a classification algorithm is used to select M-type training samples with a small distance from the test samples. And in the second stage, the selected M-type training samples are used as a new training sample set for the second-stage recognition. To increase the recognition speed, an algorithm that can select M-type training samples quickly is proposed. First, a k-means clustering algorithm is used to aggregate the training samples into a large cluster. For a new test sample, the distance between the centers of each large cluster is calculated; then, several large clusters that are closer to the test sample are selected. The categories of these large clusters are included in the new training set. Training samples with the corresponding categories are combined to form a new training sample set that is used for the second-stage recognition. Experiments on different face databases confirm that the proposed algorithm can achieve faster recognition speed based on the slightly improved recognition rate.