Image Processing Reference

In-Depth Information

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

applied on the data and features are extracted. Last step is to classify the data using Euclidian

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.

Search WWH ::

Custom Search