计算机工程与应用Issue(11)：154-158,217,6.DOI:10.3778/j.issn.1002-8331.1311-0462
LDE融合光谱回归分类的光照变化人脸识别
Fusion of spectral regression classification with LDE for face recognition with illu-mination variation
周柏清 1任勇军2
作者信息
- 1. 湖州职业技术学院 信息工程分院，浙江 湖州 313000
- 2. 南京信息工程大学 计算机与软件学院，南京 210044
- 折叠
摘要
Abstract
The performance of face recognition with illumination variation is impacted seriously by using traditional spec-tral regression algorithms to extract features, so an algorithm of spectral regression classification optimized by local dis-criminative embedding is proposed. Feature vectors of training samples are calculated. Local discriminative embedding is used to construct embedding needed by classification and embeddings needed by sub-manifold of each classification is learned based on neighbor and classification relationship. Spectral regression classification algorithm is used to compute project metrics, nearest neighbor classifier is used to finish face recognition. The effectiveness and robustness of proposed algorithm has been verified by experiments on the two common face databases extended YaleB and CMU PIE. Experimental results show that proposed algorithm has higher recognition accuracy, better operating characteristic and simpler calculate complexity clearly than several other spectral regression algorithms.关键词
光照变化/人脸识别/局部判别嵌入/光谱回归分类/最近邻分类器Key words
illumination variation/face recognition/local discriminant embedding/spectral regression classification/nearest neighbor classifier分类
信息技术与安全科学引用本文复制引用
周柏清,任勇军..LDE融合光谱回归分类的光照变化人脸识别[J].计算机工程与应用,2014,(11):154-158,217,6.
