# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 4 Experimental Results
### Experiment Analysis  
Experiments are conducted and comparison is made between our method and four state-of-the-art methods including Angle Matching Filter (AMF) [13], walking point (WP) [14], singular point block classification method (SPBC) [15], singular point object detection (SPOD) [16] and SP-Net [17]. The experiments are performed under similar conditions and followed all the rules of SPD2010. The experiment results are shown in Table 1.  
From Table 1, it can be seen that: (1) for the corrected detection rate, which is the most important index, the best result of the existing method is 38%, while that of ours is 48%--our proposal achieves about 10% improvement in the corrected detection rate. (2) For the detection rate of cores and deltas, the best results of the existing method are 56% and 60%, respectively, while our method has achieved 68% and 63%. Our approach has achieved the best results on both indexes. (3) For the miss rate of cores and deltas, the lower this rate is, the better the performance is. Our method has also obtained the best result with respect to the miss rate. (4) For the false alarm rate of cores and deltas, the WP works best, which are as low as 10% and 12%, respectively. In comparison, the rates of our proposal are 16% and 15%, respectively. However, WP's corrected detection rate is only 32%, in the case of low corrected detection rate, it does not perform well in the overall detection performance. Taken together, comprehensively considering various indexes, our method performs best in fingerprint singular point detection accuracy.  
In Figure 7, we present three examples of correct detection results by the proposed method. All these three examples are high-quality fingerprint images. In Figure 7, the red star represents the core and the yellow triangle represents the delta. In Figure 8, we present the detection results obtained by different methods on two low-quality fingerprint examples in the test dataset from SPD2010. Figure 8a,c show the core detection results on No.0311 and No.0436 in the testing dataset. The red star is the ground truth, the green star is the result obtained by the proposed method, the yellow star  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{
\begin{tabular}{c} **Corrected** \\ **Detection Rate (\%)** \\ \end{tabular} } & \multicolumn{2}{c}{**Detection Rate (\%)**} & \multicolumn{2}{c}{**Miss Rate (\%)**} & \multicolumn{2}{c}{**False Alarm Rate (\%)**} \\ \cline{2-7}  & & **Cores** & **Deltas** & **Cores** & **Deltas** & **Cores** & **Deltas** \\ \hline AMF [13] & 38 & 52 & 60 & 48 & 40 & 56 & 28 \\ WP [14] & 32 & 42 & 53 & 58 & 47 & **10** & **12** \\ SPBC [15] & 31 & 48 & 45 & 52 & 55 & 32 & 26 \\ SPOD [16] & 35 & 56 & 48 & 44 & 52 & 30 & 35 \\ SP-Net [17] & 36 & 55 & 52 & 45 & 48 & 26 & 31 \\ Proposed Method & **48** & **68** & **63** & **32** & **37** & 16 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection performance over the SPD2010 test dataset.  
is the result obtained by SPOD, the orange star is the result obtained by SP-Net, the blue star is the result obtained by AMF and the black circle means the corrected detection area. Figure 8b,d show the core detection results obtained by the proposed method. As can be seen from Figure 8, AMF, SPOD and SP-Net detect error singular points, and WP and SPBC cannot even detect any point, while our method can correctly detect the singular points. To some extent, it can be concluded that our method is more effective and robust for detecting the singular points out of low-quality fingerprints. In Figure 9, we present the numbers of correctly detected fingerprint images of our method and other five methods in the test dataset from SPD2010, where A is the total number of the testing set, B is the number of correctly detected fingerprint images by our proposed method, C is the union number of correctly detected fingerprint images by the other five methods, and D is the intersection of B and C.