Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
This work addresses the need for better nonlinear methods in computer vision applications like face recognition, but it appears incremental as it builds on existing kernel PCA and ASM techniques.
The paper tackles the problem of improving nonlinear dimensionality reduction and feature extraction by applying kernel PCA to face recognition and active shape models, showing experimental results that compare its performance with standard PCA for classification and face model construction.
Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experimental results to compare the performance of kernel PCA and standard PCA for classification problems. We also implement the kernel PCA-based ASMs, and use it to construct human face models.