核耦合度量学习方法及其在步态识别中的应用

Kernel Coupled Metric Learning and Its Application to Gait Recognition

针对已有基于线性变换的耦合度量学习方法在解决实际问题时会遇到维数灾难和无法很好描述非线性模型等问题,通过引入核方法,提出核耦合度量学习方法.首先采用非线性变换将来自不同集合的数据投影到同一个高维耦合空间,使两个集合中具有相关关系的元素投影后尽可能接近.然后在这个公共的耦合空间使用传统的核方法进行运算.最后将其应用到步态识别中,以解决步态识别中不同集合间的匹配问题.采用CASIA(B)步态数据库进行实验分析,结果表明文中方法取得较满意的识别效果.

To solve the problems of the disaster of dimensionality and the shortage on describing the nonlinear model that the linear coupled metric learning has when solving practical problems, the kernel coupled metric learning is proposed by introducing kernel method. Firstly, the nonlinear transformations are used to map the data from different sets into a high-dimensional coupled space to make the elements of two sets with correlation as close as possible to each other after the projection. Then, the traditional kernel method is used in the public coupled space. The proposed method is applied to gait recognition to solve the match problem of different sets. Experiments and analysis are made on the CASIA （B） gait database, and the experimental results show that the proposed method has satisfactory recognition results.