Biometric analysis for finger vein data - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 326

CHAPTER 25

Biometric analysis for inger
vein data

Two-dimensional kernel principal component

analysis

Sepehr Damavandinejadmonfared; Vijay Varadharajan Department of Computing, Advanced Cyber Security Research Centre,
Macquarie University, Sydney, New South Wales, Australia

Abstract
In this paper, a whole identification system is introduced for finger vein recognition. The proposed al-
gorithm first maps the input data into kernel space, then; two-dimensional principal component analysis
(2DPCA) is applied to extract the most valuable features from the mapped data. Finally, Euclidian dis-
tance classifies the features and the final decision is made. Because of the natural shape of human ingers,
the image matrixes are not square, which makes them possible to use kernel mappings in two different
ways—along row or column directions. Although some research has been done on the row and column
direction through 2DPCA, our argument is how to map the input data in different directions and get a
square matrix out of it to be analyzed by 2DPCA. In this research, we have explored this area in details
and obtained the most significant way of mapping finger vein data which results in consuming the least
time and achieving the highest accuracy for finger vein identification system. The authenticity of the res-
ults and the relationship between the finger vein data and our contribution are also discussed and ex-
plained. Furthermore, extensive experiments were conducted to prove the merit of the proposed system.

Keywords

Biometrics
Finger vein recognition
2D Principal component analysis
Kernel principal component analysis