Effective kernel mapping for one-dimensional Principal Component Analysis in finger vein recognition
Sepehr Damavandinejadmonfared, Vijay Varadharajan · 2014
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 has been proven novel in many different areas. A lot of improvements have been proposed on PCA such as Kernel Principal Component Analysis, 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 1D-PCA. Extensive experiments have been conducted for this purpose using Polynomial, Gaussian, Exponential and Laplacian Principal Component Analysis (PCA) in 4 different examinations to determine the most significant one.