# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 4 Experimental Results
### Discussion of Results  
In this work, the recognition rate (or accuracy) is used as an assessment parameter to evaluate the performance of the proposed fingerprint verification system. Recognition accuracy (ACC) is usually expressed as a ratio that can be easily calculated by dividing the number of fingerprints recognized by the total number of fingerprints presented:  
\[\text{ACC}=\frac{\text{Number of fingerprints recognized}}{\text{Total number of fingerprints presented}} \tag{18}\]  
As stated earlier, extensive tests and experiments were carried out based on two public fingerprint databases of FVC2000 DB1 FVC2002 DB1 to demonstrate the effectiveness and robustness of the proposed verification system. The average recognition accuracy is computed as a measure of the overall performance of the presented system. The testing results for both datasets are presented in detail in Tables 1 and 2.  
In light of these results, it can be observed that the verification system performs consistently well on both datasets, having relatively better performance on the FVC2000 dataset (with an overall recognition rate of 95.5%) due to its relatively high-quality fingerprint images. Additionally, it should be interesting to perform a comparative performance analysis  
Figure 9: Matching results between two fingerprint pairs, where similarity (**a**) =0.0588 (**b**) =0.75.  
between the effectiveness of the proposed verification method and other related work in terms of average recognition accuracy. The outcomes of this comparison are reported for FVC2000 DB1 and FVC2002 DB1 in Tables 3 and 4, respectively.  
From the figures presented in the tables above, it can be clearly seen that the proposed method performs notably better than other related state-of-the-art methods. All experiments and computations in this study were carried out using MATLAB (R2016a) software (on an Intel Core i5-4570 3.20 GHz, RAM 8 GB PC running Windows 10 Professional 64 bit). The total computation time required for the whole fingerprint recognition process comprises three parts: the time spent for pre-processing contrast enhancement plus the time spent for feature extraction plus the time for feature matching, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**User** & **Recognized Samples** & **ACC (\%)** \\ \hline
1 & 8 & 100 \\
2 & 8 & 100 \\
3 & 8 & 100 \\
4 & 6 & 75 \\
5 & 7 & 87.5 \\
6 & 8 & 100 \\
7 & 6 & 75 \\
8 & 7 & 87.5 \\
9 & 8 & 100 \\
10 & 7 & 87.5 \\ \hline Total & 76 & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recognition results on FVC2000 DB1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Recognition Accuracy**} \\ \cline{2-5}  & **Our Method** & **[PERSON] et al. [32]** & **[PERSON] et al. [10]** & **[PERSON] et al. [8]** \\ \hline DB1-FVC2000 & 92.5\% & 82.95\% & 82.43\% & 80.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition accuracy comparison on FVC2002 DB1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Recognition Accuracy**} \\ \cline{2-5}  & **Our Method** & **[PERSON] [7]** & **[PERSON] et al. [13]** & **[PERSON] et al. [33]** \\ \hline DB1-FVC2002 & 95\% & 94\% & 91.10\% & 81.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy comparison on FVC2000 DB1.