# Fingerprints Classification through Image Analysis and Machine Learning Method
## 5 Result of Experimentation
### Analysis Results of Experimentation  
In this article, factors (Accuracy, Precision, Recall) are used to evaluate the performance of a fingerprint classification system using machine learning methods (Random Forest algorithm, Support Vector Machine). Figure 6: Block diagram of fingerprint classification using method proposed. Figure 7: Examples of fingerprint in database. * Accuracy is a system to measure the degree of closeness of measurements of a quantity to that quantity's actual (true) value. \[Acc=(Truepositive+TrueNegative)/(Totalnumberofelements)\]
* Precision is the fraction of retrieved documents that are relevant to the find: \[Precision=(TruePositive)/(Truepositive+FalsePositive)\]
* Recall in information retrieval is a fraction of the documents that are relevant to a query that is successfully retrieved. \[Recall=(TruePositive)/(TruePositive+FalseNegative)\]  
The results shown in Figures 8-11 demonstrated that the algorithms work stably and with high accuracy. For example, NIST-DB4  
To demonstrate the robust accessibility of the two proposed machine learning algorithms--(Random Forest (RF) and Support Vector Machine (SVM)--we have experimented with 3 data sets with 2 models of CNN and k-NN. The CNN model is trained with a learning rate of 0.01 using a softmax activation function in pooling class. The Relu trigger function is used for all the hidden layers of the convolutional layer, the most learning times of the network is 100. The basic network architecture consists of--Input layer-Layers [Convolution layer-MaxPooling class-Activation class]-Output layer. When training the network, we chose parameter Batch size = 32 to select the sample size to be learned and to adjust the parameters in the network layers to have the best accuracy. The maximum number of network training sessions is 100. The model with the best classification results on the training set will be selected and tested on test sets for evaluation. We use the kNN model to experiment with many k parameters (k = 1, 3, 5, 7) to choose the best results. In the model aggregation method, we ran experiments with different numbers of decision trees to select the best results. The number of decision trees tested was from 150 to 300 trees. Through experiments, we can see that the RF algorithm for building 200 decision trees will ensure high accuracy. The SVM algorithm uses a linear multiplier function that has the best results for MGE data sets. The results are obtained from classification models (with optimal parameters) as shown in Tables 1-3 and ROC analysis Figure 12. The tables show the effect of increasing the number of trees in the ensemble. For both, increasing trees requires more time to learn but also provide better results in terms of Mean Squared Error (MSE) is calculated as follows in Equation (5):  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(f(x_{i})-y_{i})^{2}, \tag{5}\]  
where \(n\) is the number of test examples, \(f(x_{i})\) the classifier's probabilistic output on \(x_{i}\) and \(y_{i}\) are actual labels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RF** & **SVM** & **CNN** & **k-NN** \\ \hline 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 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of classification algorithms: RF, SVM, CNN, k-NN of BD-HLG database. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RF** & **SVM** & **CNN** & **k-NN** \\ \hline 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 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of classification algorithms: RF, SVM, CNN, k-NN of FVC database. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RF** & **SVM** & **CNN** & **k-NN** \\ \hline 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 \\ \hline \hline \end{tabular}
\end{table}