We propose Two Subspace-based Kernel Local Discriminant Embedding (TSKLDE) for feature extraction and recognition. The pro- cedure of TSKLDE is divided into two stages. The first stage applies the Kernel Principal Component Analysis (KPCA) to the raw data. Based on the output of KPCA, the second stage seeks two kinds of discrimi- nant information, regular and irregular. These are based on transforms derived from the within-class locality preserving scatter. The resulting framework retain the advantages of supervised global and local tech- niques. Besides, it is an inductive nonlinear technique in the sense that novel examples or images can be directly mapped. The proposed algo- rithm was tested and evaluated using three public face databases. The experimental results show that the proposed TSKLDE outperforms other Kernel based algorithms.
['Fadi Dornaika', 'Alireza Bosaghzadeh']
Two Subspace-Based Kernel Local Discriminant Embedding