Biometric analysis for finger vein data - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 328

on 60 × 60 or 180 × 180 matrixes will lead to a great difference in accuracies and time duration
for the system.
The remaining of this paper is organized as follows: in Section 2 , image acquisition is ex-
plained. In Section 3 , 2D principal component is introduced briefly. In Section 4 , kernel map-
ping along row and column direction is explained. In Section 5 , finger vein recognition al-
gorithm is proposed. In Section 6 , experimental results on the finger vein database are dis-
cussed. Finally, Section 7 concludes the paper.
2 Image Acquisition
In this section, we briefly explain how the finger vein images were captured and what devices
were used building the scanner. Based on the proven scientific fact that the light rays can be
absorbed by deoxygenated hemoglobin in the vein, absorption coefficient of the vein is higher
than other parts of finger. Having said that we designed a scanner consisting of four low-cost
prototype devices such as infrared LEDs (830 nm wavelength) and the control circuit to drive
the LEDs properly, a camera to capture the images, an infrared pass filter and a computer to
process the images. To make the camera sensitive to the IR light, there have been some modi-
ications to it; the IR blocking filter was removed and replaced with an IR pass filter which
blocks visible wavelengths of light and passes the IR light ( Figure 1 ).
FIGURE 1 A subset of samples captured from a subject.
3 Two-dimensional principal component analysis
The main idea of two-dimensional principal component analysis (2DPCA) is to project the im-
age A , which is represented as a random m × n matrix, onto X that is an n -dimensional unitary
column vector:
(1)
Therefore, the projected feature vector of image A is achieved from Equation (1) . Finding
the appropriate projection vector ( X ) is the goal. To evaluate the discriminatory power of X ,
the total scater of projected samples can be used, which could be characterized by tracing the
covariance matrix of projected features vectors. The following criterion is introduced from this
point of view: