基于重叠空间相对划分的仿生模式识别方法

Relative Division of Overlapping Space Based Biomimetic Pattern Recognition

为实现特征空间的有效覆盖,提出在高维空间覆盖前提下基于重叠空间相对划分的仿生模式识别方法(RDBPR),实现在认识的前提下对样本进行相对区别.该方法在多样本大阈值造成的空间重叠的情况下,通过计算到各类特征子空间的相对距离,对重叠空间中的样本进行相对划分,从而可在不增加误识的基础上提高正确识别率.以人脸识别为例,实验证明,本文方法在稳定性、识别率方面均优于传统识别分类器.

To realize the effective coverage in feature space, a method of relative division of overlapping space based biomimetic pattern recognition （RDBPR） is proposed in high dimensional space. It can relatively classify the samples on the basis of cognition. In the overlapping space caused by big threshold value, the sample is classified by calculating the distance to the relative subspace, and thereby the correct recognition rate can be improved with low misclassification rate. The experimental results of face recognition show RDBPR has higher correct recognition and better stability than the traditional classifiers.