Biometric analysis for finger vein data - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 332

and we have more information to analyze by 2DPCA. Figure 2 shows the detailed diagram of
the kernel mapping and 2DPCA on the mapped data in two different directions. It is observed
from the diagram that by applying the kernel function from row or column direction, the ker-
nel matrix ( K ) is squared and with dimension of n or m .
FIGURE 2 Flow diagram of kernel mapping along row and column direction and applying
2DPCA.
This argument is indispensable because the dimension of the data affects the output of the
2DPCA greatly. Having higher dimension and more information and features does not guar-
antee ending up more promising results and higher accuracies. Furthermore, the higher the
dimension, the more time consuming the system is. On the other hand, there has to be a bal-
ance between the dimension of the data, the number of used features, and the algorithm that
is used to analyze the data.
5 Finger Vein Recognition Algorithm
Our proposed finger vein recognition algorithm is explained in this section. As it is shown in
Figure 3 , the algorithm consists of five steps; first step is to extract the region of interest (ROI).
Second one is to normalize the images. Third step is to map the data into kernel space along
the row and column directions which was explained in Section 4 . In the fourth step, 2DPCA is
applied on the data and features are extracted. Last step is to classify the data using Euclidian
distance. The flow diagram of the proposed algorithm is indicated in Figure 3 . All steps except
for steps 3 and 4, which were explained in Section 4 , are introduced in the succeeding part of
this section.
FIGURE 3 Flow diagram of the proposed algorithm.
5.1 ROI Extraction
The unwanted black area around the images should be cropped as this area reduces the ac-
curacy and is considered as nothing but noise. To crop images optimally, the used algorithm
consists of three major steps. First of all, the edge is detected. Using the detected edges two
horizontal lines are determined and the image is cropped horizontally according to the detec-
ted lines. Last but not least, the image is cropped vertically at 5% from the left border and 15%
from the right border.