摘要
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 bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.
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.
基金
National "863" project (2001AA114140)
the National Natural Science Foundation of China (60135020).
