Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

TL;DR

This paper reviews kernel PCA, explores pre-image reconstruction, and demonstrates its applications in face recognition and active shape models, showing improved performance over traditional PCA.

Contribution

It introduces kernel PCA applications in face recognition and active shape models, including pre-image reconstruction and experimental performance comparisons.

Findings

Kernel PCA outperforms standard PCA in classification tasks.

Kernel PCA-based active shape models improve face modeling accuracy.

Experimental results validate the effectiveness of kernel PCA in practical applications.

Abstract

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.