Getting Started with MATLAB

MATLAB is Package of different toolboxes such as image processing toolbox, signal processing toolbox, communication system toolbox, computer vision toolbox, control system toolbox and many more. Apart from these installed toolboxes, Add-on toolboxes are also available separately for solving the particular problems which extends the MATLAB environment. MATLAB is basically used for computation of various problems and equation expressed in mathematical notations. Being simple to use another advantage of MATLAB is that it provides GUI development with predefined dialog box, user interface objects and GUI utilities.

 

 

MATLAB Window

The MATLAB window is broadly divided into five main windows.

1.  Current Folder: Here from the picture above you can see that the default folder that is the current folder is bin where your files or work will be stored. You can also save your files to the desired folder by providing a proper link or path to that folder in current folder. There are other MATLAB commands for checking the current directory pwd (present working directory) and changing the directory cd.

2. Command Window: As the name suggests this window is using for executing the inbuilt MATLAB commands, particular m files using the program name or functions or directly access or creating the variables. Also the output of the programs or commands is shown here if the commands are not terminated by semi colons (;).

3. Editor: Like all other editors the MATLAB editors is also used for editing m files, script or functions. Creating a new m files or script will land you here. Different colors are used here to improve the readability of codes like green color is used for comments.

4. Workspace: The workspace stores the various variables that were created or stored during the program execution or we can say that this window shows the memory content after the program execution. It also provides different options such as creating a variable with single click, import variable, delete variable, save variable and plot the variable values.

5. Command History: It displays the list of all the previously entered commands irrespective of whether the commands were successful or not. The commands are listed with the dated and time which makes it easier to scroll to the particular commands.

 

Experimental Results of DoG

So far we have seen the experimental results of DAPM and here we will be seeing the experimental results of DoG pore model. Also the experiment was conducted on a high resolution partial fingerprint database using the proposed pore extraction and pore matching methods and implemented in the MATLAB software's image processing toolbox.

  

Pore Detection Accuracy

We first assess the pore detection accuracy of the Difference of Gaussian. For this purpose we choose a set of 10 fingerprint images from the database. Fig.1 shows the examples of pore extraction result of the two pore extraction model. In addition to the visual evaluation of the pore detection results, we calculated the pore detection accuracy on the 10 fingerprint images by using the two metric: True Detection Rate (TDR) and False Detection Rate (FDR).

A good pore extraction should have a high TDR and a low FDR simultaneously. Table 1 list the average detection accuracy of the two pore extraction considered as a true pore whereas the one which lies outside the ridge i.e. on valley are considered as false pores.

Table 1: Performance Measure of Difference of Gaussian

Images	FDR	Percentage	TDR	Percentage
Image_1	42/473	08.87 %	276/473	73.15 %
Image_2	41/389	10.53 %	338/389	86.88 %
Image_3	38/448	08.48 %	359/448	80.13 %
Image_4	29/422	06.87 %	341/422	80.80 %
Image_5	54/456	11.84 %	375/456	82.23 %
Image_6	35/412	08.49 %	345/412	83.73 %
Image_7	47/491	09.57 %	409/491	83.29 %
Image_8	54/374	14.43 %	276/374	74.60 %
Image_9	17/363	04.68 %	316/363	87.05 %
Image_10	26/320	05.50 %	280/320	87.50 %

 

 The fig 2 below shows the results of the Difference of Gaussian algorithm on four different partial fingerprint images. The extracted pores using DoG algorithms is marked with red circles.

 

Fig.2: Results of Difference of Gaussian pore extraction; (a),(c),(e) and (g) are input images;  (b), (d), (f) and (h)  shows detected pores on input images.

Here from the experimental results we can see that the true detection rates of the two pore models are more than sufficient. Also the false detection rate is low. The numbers of pores extracted from the two pore models are sufficient for reliable matching of pores. As seen from the Fig1 the number of pore are more than the average number of minutiae which testified our claim of higher feature count than minutiae count including ridge ending and bifurcations. Thus the proposed Dynamic Anisotropic Pore Model and Difference of Gaussian model has yield the sufficient features i.e. pores which when used with direct pore matching method will overcome the traditional fingerprint recognition technique using  minutiae   features.