摘要
为了提高步态识别问题的识别性能,将"核技巧"应用到步态识别上,对核二维线性判别分析提出新的解决方案,在自建的HEU(B)步态数据库上,应用核主成分分析、核线性判别分析、核二维主成分分析与核二维线性判别分析进行特征提取作对比实验研究.实验结果显示:"核技巧"用于矩阵特征比向量更有效;核二维主成分分析对于单训练样本较核主成分分析更为有效;核二维线性判别分析在测试识别时间上有优势.
A kernel trick was applied to gait recognition in order to improve recognition performance. A novel solution was proposed for kernel two dimensional linear discriminant analysis. Feature extraction, which makes use of kernel principal component analysis （KPCA）, kernel linear discriminant analysis （KLDA）, kernel two dimensional principal component analysis （K2DPCA）, and kernel two dimensional linear discriminant analysis （K2DLDA）, was performed for contrasting experimerks in HEU （B） ＇ s locally built gait database. The experimental results demonstrate that a kernel trick applied to a matrix form is more efficient than in vector form. K2DPCA outperforms KPCA significantly with a single sample per person, and K2DLDA has the advantage of less time spent on recognition testing.
出处
《智能系统学报》
2011年第1期63-67,共5页
CAAI Transactions on Intelligent Systems
基金
国家"863"计划资助项目(2008AA01Z148)
关键词
步态识别
核主成分分析
核线性判别分析
核二维主成分分析
核二维线性判别分析
gait recognition
kernel principal component analysis （KPCA）
kernel linear discriminant analysis （KLDA）
kernel two dimensional principal component analysis （K2DPCA）
kernel two dimensional linear discriminant analysis （K2DLDA）
