计算机工程2011，Vol.37Issue(15)：137-139,3.DOI:10.3969/j.issn.1000-3428.2011.15.043
基于2D-Gabor与KLDA的特征提取
Feature Extraction Based on 2D-Gabor and Kernel Linear Discriminant Analysis
摘要
Abstract
This paper proposes a feature extraction method combining 2D-Gabor wavelet and Kernel Linear Discriminant Analysis(KLDA). The pretreated face images are filtered with multi-scale and multi-orientation, and the filtered images are added into the original face database as separate samples to increase the number of samples. The classical KLDA method is applied to extract features once more to obtain the ideal sample characteristics of class cohesion and between-class scatter. Third-order nearest neighbor classifier is used to classify the features. Experimental results indicate that the method can get a better performance and recognition rate, and it is easy to implement in projects.关键词
人脸识别/2D-Gabor小波/核线性鉴别分析/类内聚度/类间散度Key words
face recognition/2D-Gabor wavelet/Kernel Linear Discriminant Analysis(KLDA)/class cohesion/between-class scatter分类
信息技术与安全科学引用本文复制引用
张建明,杜丹,刘俊宁..基于2D-Gabor与KLDA的特征提取[J].计算机工程,2011,37(15):137-139,3.基金项目
国家自然科学基金资助项目(60673190) （60673190）
江苏省自然科学基金资助项目(BK2009199) （BK2009199）
