Image Processing Reference
In-Depth Information
5.2 Image Normalization
In order to achieve the highest accuracy in least time, images are normalized to smaller size
after ROI extraction. It is obvious that the smaller the size, the faster the system is. However, if
the size of the image is too small, it may cause too much loss of information as well. Therefore,
there has to be a balance between size of the images and the accuracy of the system. Based on
our experiments, when using 2DPCA to extract the features, the optimal size of finger vein im-
ages resulting in both least time consumption and highest accuracy is 20 × 60. Thus, all images
are normalized into 20 × 60.
5.3 Feature Extraction and Classification Method
As it was mentioned before, Euclidian distance is used as a classifier in this system. Euclidian
distance is a very fast method which, we believe, is appropriate for this system because after
using kernel map and 2DPCA, the dimension of the data is reduced and therefore the Euclidi-
an distance is sufficient to be used.
Given an image sample A and the optimal projection axes (selected eigenvectors,
X 1 , X 2 , …, X d ), the projection will be as follows:
Using d axes to project the data onto, we will get d projected feature vectors Y 1 Y 2 , …, Y d . These
vectors are the principal component of the sample image A . Puting these vectors in the form
of a matrix, we will get feature matrix of the image A , which is m × d , B = [ Y 1 , …, Y d ].
Then, a nearest neighbor classifier is used to classify the data after transferring all images
by 2DPCA and obtaining the feature matrix of them. Considering B i = [ Y 1 i , Y 1 i , …, Y d i ] and
B j = [ Y 1 i , Y 1 i , …, Y d i ], the Euclidian distance between them is defined as follows:
6 Experimental results on finger vein database
In this section, the experiments conducted on finger vein data are given and explained. Ex-
perimental results are explained in two subsections; column direction analysis in Section 6.1
and row direction analysis in Section 6.2 . Our database consists of 10 samples for each of 100
individuals which results in a total number of 1000 images. In each different part of the exper-
iments, three different types of training and testing were used. The size of image samples also
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