Effective finger vein-based authentication: kernel principal component analysis

Kernel functions have been very useful in data classification for the purpose of identification and verification so far. Applying such mappings first and using some methods on the mapped data such as principal component analysis (PCA) has been proven novel in many different areas. A lot of improvements have been proposed on PCA, such as kernel PCA, and kernel entropy component analysis, which are known as very novel and reliable methods in face recognition and data classification. In this paper, we implemented four different kernel mapping functions on finger database to determine the most appropriate one in terms of analyzing finger vein data using one-dimensional PCA. Extensive experiments have been conducted for this purpose using polynomial, Gaussian, exponential, and Laplacian PCA in four different examinations to determine the most significant one.