摘要
提出利用类内子流形对高维人脸图像进行判别分析的新方法,沿对齐的类间局部间隔法向扩展类内子图,获得一系列线性投影,并正交化线性投影得到一组子空间的正交基向量。使用局部相邻关系增大类间差异,并将类内结构保存到与类间间隔区域对齐的子空间中,能有效降低因人脸图像拍摄角度、姿态、光照、眼镜和性别等因素导致的数据多模态或低维流形的高曲率对识别性能的影响。在Yale Face Database B和UMIST face database上进行的实验证明,较之LPP和FDA等方法,该方法能更加可靠地保留类内和类间的子流形结构,且有更高的识别准确率。
This paper proposes a new approach to perform the discriminant analysis on the labelled high dimensional image data with intra-class sub-manifolds.Real world images are usually taken from the different camera views.Pose,illumination,glasses and gender of the persons taking the facial images usually lead to multi-modality or high curvature of the underlying manifold structures.These variations result in the degraded performance of many existing algorithms.This paper proposes to preserve the within-class local structure,while imposing constrain on the variances only in the directions normal to the between-class margin.The experiments on Yale-B and UMIST face database show that the proposed algorithm outperforms many approaches such as LPP（locality preserving projections） and FDA（fisher discriminant analysis）.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2010年第6期915-919,共5页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(60973070)
四川省产业技术与开发项目
关键词
判别分析
人脸识别
特征提取
图像分类
discriminant analysis
face recognition
feature extraction
image classification
作者简介
作者简介：蒲晓蓉（1969-），女，博士。主要从事生物特征识别、计算智能等方面的研究．
