局部Bagging方法及其在人脸识别中的应用

Bagging方法无法在稳定的分类器(如最近邻分类器)上构建多样的分量分类器,因此它不适合于稳定的分类器.同时,小样本特性Bagging也很难应用于人脸识别等任务中.本文提出了一种局部Bagging(L-Bagging)方法以同时解决上述两个问题.L-Bagging和Bagging的主要区别是L-Bagging在每个事先划分好的局部区域内进行自助集的采样而不是如Bagging那样在原始的样本集上采样.由于局部区域的维数通常远远小于训练样本数,并且分量分类器又是构建在不同的局部区域上的, 因此 L-Bagging方法不仅有效地解决了小样本问题,而且产生了更多样的分量分类器.在4个标准的人脸数据库(AR,Yale,ORL和Yale B)上的实验结果表明所提出的L-Bagging方法是有效的,并且对光照、遮挡及轻微的姿态变化是鲁棒的.

LOCAL BAGGING AND ITS APPLICATION ON FACE RECOGNITION

Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size (SSS) property of face recognition.To solve the two problems,local Bagging (L-Bagging) is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition.The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image.Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers.Experimental results on four standard face image databases (AR,Yale,ORL and Yale B) indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation.