So fingerprint recognition is part of security enforcement and essentially had to be developed. This project focuses on the effectiveness of the Gray-Level Co-occurrence Matrices (GLCM) and Discrete Wavelet Transform (DWT) techniques for fingerprint recognition. As in chapter one, the author discusses the background of the GLCM and the DWT, as well as why this project was initiated.
Apart from that, the author also discusses about the problem that had been faced earlier to recognize fingerprints optimally. In the next chapter, the history of GLCM as well as DWT had been widely discussed, which made the fingerprint recognition system more popular today. The definition of term, equation and equation related to GLCM and DWT had also been explained.
In the third chapter, the author reviews the method that will be approached for the project for the entire eight-month time frame. As for the last chapter, several initial conclusions were drawn about the techniques for fingerprint recognition.
INTRODUCTION
- Background of Study
- History
- Problem Statement
- Objectives
- Scope of Study
- Relevancy of Study
- Feasibility of Study
Over the centuries, the Department of Justice has used fingerprint or thumbprint matching for security purposes. Today's technology has developed new approaches to suit identity management as well as access control regarding fingerprint or thumbprint identification or rather recognition. In the early 20th century, several conventional scientists such as Henry Faulds, Francis Galton, and Edward Henry began to develop a fingerprint recognition approach for knowledge development purposes.
Among the early developments are murder, crime and offender identification using fingerprint recognition [5]. Nevertheless, the largest fingerprint recognition system had appeared at the end of the 20th century and had been developed by the Integrated Automated Fingerprint Identification System (IAFIS). The collected information is included in the demographic statistics as well as complete with 10 fingerprint indexes [5].
Gray-level co-occurrence matrices (GLCM) have been on the scene for nearly forty years and are still widely used today. The last phase is to evaluate the performance of the system measured in terms of correct detection.
LITERATURE REVIEW
Definition
GLCM
In addition to that, GLCM has also been used as discrete Fourier transform normalization to convert rotation-dependent features into rotation-invariant ones and tested on four different datasets of natural and synthetic images. The goal can be achieved by considering all pixels located approximately at a given distance from it, extracting rotation-dependent features for each direction defined by the neighborhood and converting the rotation-dependent features to rotation-independent ones" (Francesco Bianconi. Many quantitative measures of texture exist and use 3D co-occurrence matrices in CBIR applications.
The purpose of the related paperworks is to generalize the concept of co-occurrence matrices to dimensional Euclidean spaces and to extract more features from the matrix.
DWT
If the signal is reconstructed by an inverse low-pass filter of the form , the result is a duplication of each input from the low-pass filter output. Since the perfect reconstruction is the sum of the inverse low-pass and inverse high-pass filters, the output of the inverse high-pass filter can be calculated. The first step involved understanding the computation involved in a multi-dilation wavelet transform and determining the best structure for the SPROC chip, a digital signal processing chip that utilizes parallel processing and pipelining for efficiency.
The SPROC chip is basically a RISC processor with an instruction set targeted at DSP applications [15]. Although it seemed fairly certain that the final version of the wavelet transformer would be a grating filter, matrix methods were studied to obtain a basic understanding of wavelets, the results of which are presented in the discussion of Chapter 4.
METHODOLOGY
Project Activities
The total and division of the GLCM number values is evaluated after each normalizing GLCM value in the cell contents is multiplied by a factor [6]. The equation will especially be used to calculate the weight of the pixel in the captured image. There is no contrast created in the diagonal GLCM table, but the contrast will increase as the value from the diagonal increases, which is also affected by increasing the factor.
The dissimilarity chi-square equation (5) will then be used to track the dissimilarity between the original database fingerprint as well as the captured image of the fingerprint. This will expand a digital signal and each pixel in the image will be expanded by a decimator [15]. This data is passed through two convolution functions, each of which creates an output stream that is half the length of the original input.
While the low and high pass outputs in the case of the grating filter are usually referred to as odd and even outputs respectively [16]. The event or high-pass output contains the difference between the true input and the value of the reconstructed input if it were to be reconstructed only from the information given in the odd output. However, each output has half the frequency band of the input, so the frequency resolution has been doubled.
For the first block, it indicates that the entire fingerprint will be stored in a database in a. The next block indicates that the entire fingerprint will be converted to gray in color, so the size of the fingerprint database will be much smaller. This process also ensures that the line in the finger or finger back pattern is easier to trace and scan.
Third, the contrast, correlation, energy as well as homogeneity of the fingerprint will be calculated and compared with each other. And finally, the whole data set will be compared with each other to check the fingerprint dissimilarity. Then, the fingerprint image will be high-pass and low-pass filtered to account for the next process.
Project Timeline
Project Key-Milestone
RESULTS
Result
In this part of the experiment, the result of the inequality obtained varies more compared to experiment 2 and experiment 1. Table 5, 6 and 7 below show that the value of the approximation is the horizontal, vertical and diagonal coefficient respectively. Nevertheless, it turns out that the fingerprint between dataset number 13 and dataset number 20 shows the largest disparity.
Compared to the GLCM technique, DWT is more sensitive and can detect greater dissimilarity between datasets. Compared to experiment 1 and experiment 2, this experiment 2 yields the greatest dissimilarity due to the higher implied noise. But, the dissimilarity value in experiment 3 and experiment 2 are almost the same and up to 4 decimal places should be required.
For the sake of the future guilt of this project, the author strongly recommended that this project is included with the hardware unit for the application purpose. GLCM also has many other techniques that can be used for the fingerprint recognition area. Moreover, the GLCM technique can also be used for other recognition, such as face recognition, iris of eye recognition, and lung pinch detection.
In addition, in this project, a combination of GLCM and DWT technique will be performed to compare the best technique. In addition, the database will be increased to achieve higher accuracy and precision value.
CONCLUSION AND RECOMMENDATIONS
Normalization equation
This contrast group is specified to measure the associated weight or factorial contrast with respect to the distance from the diagonal of the GLCM. Contrast will become a value of zero if the integer channel is set with an 8-bit or 16-bit channel, so it should only be introduced with real numbers.
Contrast equation
Dissimilarity equation
Homogeneity equation
Dissimilarity Chi-squared equation
Low Pass Filter Equation
High Pass Filter Equation
Discrete Wavelet Transform Equation
This process will ensure that the fingerprints are easy to recall for the next process. Then, the received fingerprint will be compared with the set threshold and later it will be decided whether it can be accepted or not. During the calculation process, all horizontal, vertical and diagonal approximation coefficients will be included.
