# Fingerprints Classification through Image Analysis and Machine Learning Method

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Proposed Method

#### 3.1. Fingerprint Image Pre-Processing and Enhancement

#### 3.2. Types of Fingerprint and Features Extraction

**Arch**: These occur in about 5% of the encountered fingerprints. The identifying features of this arch are that the fingerprints have overlapping shapes that form layers and have a mountain-like peak. Arch of fingerprints are divided into several categories—AS (the lines are stacked on top of each other, unconcerned, no intersection.), AE (a combination of whorl and arch group, the distance from the center to the intersection of eagles is less than 5 veins), AU, AR (the combination of the loop group with arch, the distance from the center to the intersection is less than 5 fringe lines) as shown in Figure 3.**Loop**: It is called the loop (can be seen in almost 60% to 65% of fingerprints worldwide) fingerprint because it is shaped like a water wave with the following features—the ridges make a backward turn in loops, triangular with a center and an intersection. Divided into two types: RL—Radial Loop: Top of the triangle facing the pinky finger. It looks like a stream of water flowing downwards (on the little finger). This type accounts for about 6% of fingerprints worldwide. UL—Ulnar Loop: The top of the triangle faces the thumb. It is shaped like a stream of water flowing backward (thumb direction). This form only accounts for 2% of fingerprints worldwide. A loop pattern has only one delta as shown in Figure 4.**Whorl**: This fingerprint only accounts for about 25% to 35% of fingerprints worldwide. Whorl pattern identification is that they have one circuit and 2 Delta (intersection) as shown in Figure 5.

**Feature extraction of singularity.**On the fingerprint there are areas with unusual structures compared to other areas. They often have a parallel structure called a singularity. There are two types of singularity—core and delta. To extract singularity characteristics, we proceed as follows:

- Step 1. Input image then resize image to $256\times 256$
- Step 2. Fingerprint pre-processing and enhancement
- Step 3. At each pixel, the gradient is calculated in two directions x and y are ${G}_{x}$ and ${G}_{y}$ based on the Formula (1):$$\phi =\frac{1}{2}{tan}^{-1}\left[\frac{{\sum}_{i=1}^{W}{\sum}_{j=1}^{W}2{G}_{x}\left(i,j\right){G}_{y}\left(i,j\right)}{{\sum}_{i=1}^{W}{\sum}_{j=1}^{W}\left({G}_{x}^{2}\left(i,j\right)-{G}_{y}^{2}\left(i,j\right)\right)}\right]$$
- Step 4. Identify singularity points using the Pointcare index [15]. Pointcare index at the pixel with coordinates $\left(i,j\right)$ is the sum of the deviations of direction of adjacent points, calculated as follows in Equation (2):$$PC\left(i,j\right)=\sum _{k=0}^{{N}_{p}-1}\Delta \left(k\right)$$$$\Delta \left(k\right)=\left\{\begin{array}{c}d\left(k\right);\left|d\left(k\right)\right|<\frac{\pi}{2}\hfill \\ d\left(k\right)+\pi ;d\left(k\right)\le -\frac{\pi}{2}\hfill \\ d\left(k\right)-\pi \hfill \end{array}\right.d\left(k\right)=\phi \left({x}_{k+1},{y}_{k+1}\right)-\phi \left({x}_{k},{y}_{k}\right),$$Based on the Pointcare index, we can identify singularity points as follows in Equation (4):$$PC(i,j)=\left\{\begin{array}{c}{0}^{0};\left(i,j\right)\mathrm{is}\phantom{\rule{4.pt}{0ex}}\mathrm{not}\phantom{\rule{4.pt}{0ex}}\mathrm{sin}\mathrm{gularity}\hfill \\ {360}^{0};\left(i,j\right)\mathrm{is}\phantom{\rule{4.pt}{0ex}}\mathrm{whorl}\hfill \\ {180}^{0};\left(i,j\right)\mathrm{is}\phantom{\rule{4.pt}{0ex}}\mathrm{loop}\hfill \\ -{180}^{0};\left(i,j\right)\mathrm{is}\phantom{\rule{4.pt}{0ex}}\mathrm{delta}\hfill \end{array}\right.$$
- Step 5. Save and create fingerprint features vector.

## 4. Classification Fingerprint Based on Random Forest and Decision Tree with Singularity Features

**Parameters of Random Forest for Fingerprint Classification.**

- The number of trees to train model = 2000.
- The function to measure the quality of a split - Gini Impurity.
- Bootstrap samples = True.

**Parameters of Support Vector Machine for Fingerprint Classification.**

- Penalty parameter to measure error term = 1.0.
- Kernel: basis functions.
- Shrinking heuristic = True.

## 5. Result of Experimentation

#### 5.1. Database

#### 5.2. Analysis Results of Experimentation

- Accuracy is a system to measure the degree of closeness of measurements of a quantity to that quantity’s actual (true) value.$Acc=(Truepositive+TrueNegative)/\left(Totalnumberofelements\right)$
- Precision is the fraction of retrieved documents that are relevant to the find:$Precision=\left(TruePositive\right)/(Truepositive+FalsePositive)$
- Recall in information retrieval is a fraction of the documents that are relevant to a query that is successfully retrieved.$Recall=\left(TruePositive\right)/(TruePositive+FalseNegative)$

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**The result of image pre-processing of a fingerprint. (

**a**,

**b**) show the results of the equalization histogram and noise filter based on CNN, (

**c**,

**d**) show the results of edge detection and morphology

**Figure 9.**Analysis rate errors of Support Vector Machine algorithm in 3 databases ((

**a**) BG-HLG, (

**b**) FVC, (

**c**) NIST-BD4).

**Figure 11.**Analysis rate errors of Random Forest algorithm in 3 databases ((

**a**) BG-HLG, (

**b**) FVC, (

**c**) NIST-BD4).

RF | SVM | CNN | k-NN | |
---|---|---|---|---|

Training time (s) | 8H15M | 7H40M | 8H32M | 5H28M |

Correct rate (%) | 96.25 | 95.76 | 95.82 | 91.90 |

MSE | 0.274 | 0.321 | 0.303 | 0.481 |

RF | SVM | CNN | k-NN | |
---|---|---|---|---|

Training time (s) | 12H30M | 9H06M | 14H05M | 7H50M |

Correct rate (%) | 97.78 | 95.83 | 96.11 | 92.05 |

MSE | 0.215 | 0.304 | 0.267 | 0.668 |

RF | SVM | CNN | k-NN | |
---|---|---|---|---|

Training time (s) | 18H47M | 15H40M | 20H31M | 10H38M |

Correct rate (%) | 97.86 | 96.81 | 96.90 | 92.88 |

MSE | 0.255 | 0.317 | 0.240 | 0.719 |

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**MDPI and ACS Style**

Nguyen, H.T.; Nguyen, L.T.
Fingerprints Classification through Image Analysis and Machine Learning Method. *Algorithms* **2019**, *12*, 241.
https://doi.org/10.3390/a12110241

**AMA Style**

Nguyen HT, Nguyen LT.
Fingerprints Classification through Image Analysis and Machine Learning Method. *Algorithms*. 2019; 12(11):241.
https://doi.org/10.3390/a12110241

**Chicago/Turabian Style**

Nguyen, Huong Thu, and Long The Nguyen.
2019. "Fingerprints Classification through Image Analysis and Machine Learning Method" *Algorithms* 12, no. 11: 241.
https://doi.org/10.3390/a12110241