基于支持向量机的人脸检测训练集增强算法实现。根据支持向量机(support vector machine，简称SVM)~ ，对基于边界的分类算"~(geometric approach)~

言，类别边界附近的样本通常比其他样本包含有更多的分类信息．基于这一基本思路，以人脸检测问题为例．探讨了

对给定训练样本集进行边界增强的问题，并为此而提出了一种基于支持向量机和改进的非线性精简集算法

IRS(improved reduced set)的训练集边界样本增强算法，用以扩大-91l练集并改善其样本分布．其中，所谓IRS算法是指

在精简集(reduced se0算法的核函数中嵌入一种新的距离度量一一图像欧式距离一一来改善其迭代近似性能，IRS

可以有效地生成新的、位于类别边界附近的虚拟样本以增强给定训练集．为了验证算法的有效性，采用增强的样本

集训练基于AdaBoost的人脸检测器，并在MIT+CMU正面人脸测试库上进行了测试．实验结果表明通过这种方法

能够有效地提高最终分类器的人脸检测性能．-According to support vector machines(SVMs)，for those geometric approach based classification

methods，examples close to the class boundary usually are more informative than others．Taking face detection as an

example，this paper addresses the problem of enhancing given training set and presents a nonlinear method to tackle

the problem effectively．Based on SVM and improved reduced set algorithm (IRS)，the method generates new

examples lying close to the face／non—face class boundary to enlarge the original dataset and hence improve its

sample distribution．The new IRS algorithm has greatly improved the approximation performance of the original

reduced set(RS)method by embedding a new distance metric called image Euclidean distance(IMED)into the

keme1 function．To verify the generalization capability of the proposed method，the enhanced dataset is used to train

an AdaBoost．based face detector and test it on the MIT+CMU frontal face test set．The experimental results show

that the origina