基于核的两阶段稀疏表示的人脸识别研究

Research on Face Recognition with a Kernel-Based Two-Phase Sparse Representation Method

为了提高人脸识别的分类正确率,提出了一种基于核的两阶段稀疏表示(KBTPSR)的人脸识别方法。该方法首先利用一个非线性函数将原始数据空间映射到特征空间;然后,在该特征空间中将待测样本表示为所有训练样本的一个线性组合,接下来根据每个训练样本的表示贡献选出待测样本的M个最近邻;最后,将待测样本表示为上述M个最近邻的一个线性组合并且利用每一类训练样本对待测样本的表示贡献来完成分类。大量的实验结果表明,该方法可以获得很好的识别效果。

A kernel-based two-phase sparse representation（KBTPSR） method is proposed to improve the classification accuracy of face recognition.Firstly,the proposed method exploits a non-linear function to map raw data space to feature space.Then,the testing sample is represented as a linear combination of all the training samples in the feature space,and M nearest neighbors of the testing sample is selected according to the representation contribution of each training sample.Finally,the testing sample is represented as a linear combination of the selected M nearest neighbors and exploits the representation contribution of every class to perform classification.A large number of experimental results show that the proposed method can obtain good recognition effect.