基于加权主元分析(WPCA)的人脸识别

Face Recognition Based on Weighted PCA

将特征加权和主元分析相结合,提出了一种新的加权主元分析方法;这种方法先根据加权重建误差最小化,计算出各类训练样本的加权子空间,然后计算测试样本点到各加权子空间的距离,并根据该距离进行分类识别。最后,通过对剑桥ORL数据库进行的试验证明,该方法与传统的主元分析相比可以在不增加运算量的情况下大大提高识别率。

This paper proposes a face novel recognition method based on Weighted PCA, which combined weighted features with PCA. At first, we calculate the weighted subspace for each class by minimizing the weighted reconstruction error. Then a test example is classified by the distance from weighted subspace. The experiments on Cambridge ORL database show that our Weighted PCA method can improve the recognition rate significantly without increasing the computation, when compared with PCA.