A non-parameter bayesian classifier for face recognition |
| |
Authors: | Liu Qingshan Lu Hanqing Ma Songde |
| |
Affiliation: | Nat. Lab of Pattern Recognition, Inst. of Automation, Chinese Academy of Sciences, Beijing 100080 |
| |
Abstract: | A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE) is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN) classifier in formation. The class conditional density is estimated by KDE and the bandwidth of the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspace analysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA) are respectively used to extract features, and the proposed method is compared with Probabilistic Reasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in face recognition systems. The experiments are performed on two benchmarks and the experimental results show that the KDE outperforms PRM, NC and NN classifiers. |
| |
Keywords: | Kernel Density Estimation (KDE) Probabilistic Reasoning Models (PRM) Principal Component Analysis (PCA) Kernel-based PCA (KPCA) Face recognition |
本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录! |
| 点击此处可从《电子科学学刊(英文版)》浏览原始摘要信息 |
|
点击此处可从《电子科学学刊(英文版)》下载全文 |