液晶与显示2013，Vol.28Issue(3)：440-445,6.DOI:10.3788/YJYXS20132803.0440
B2DPCA和ELM人脸识别算法研究
Face Recognition Algorithm Based on B2DPCA and ELM
摘要
Abstract
A new human face recognition algorithm was proposed based on B2DPCA (bidirectional two dimensional principal component analysis) and ELM (extreme learning machine).This method was based on curvelet image decomposition of human faces and an improved dimensionality reduction technique.Discriminative feature sets were generated by using B2DPCA to train and test ELM classifier.The recognition accuracy can be improved by using this method.Extensive experiments were performed by using databases and results were compared with state of the existing techniques.The results showed recognition accuracy and minimal dependence on the number of prototypes were significantly improved by using B2DPCA and ELM algorithm.The local characteristics and global information based on curvelet decomposition are expected to apply to the recognition accuracy and speed of classifica tion in the future.关键词
人脸识别/双向二维主成分分析/极端学习机/降维技术/识别准确率Key words
human face recognition/B2DPCA/extreme learning machine/dimensionality reduction technique/recognition accuracy分类
信息技术与安全科学引用本文复制引用
李定珍,郭建昌..B2DPCA和ELM人脸识别算法研究[J].液晶与显示,2013,28(3):440-445,6.基金项目
河南省教育厅科技攻关项目(No.12B510024) （No.12B510024）
