# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 4 Experimental Results
### Properties Comparisons of Three Methods Based on Deep Learning  
In order to further compare the differences among the three methods mentioned above based on deep learning with ours, we analyze them from six aspects, including the network number, the network  
Figure 8: Two examples of core detection results by the five singular points detection methods and the proposed method: (**a**) the core detection results on No.0311; (**b**) the core detected by our proposal; (**c**) the core detection results on No.0436; and (**d**) the core detected by our proposal.  
Figure 7: Three examples of correct detection results by the proposed method.  
Figure 9: Numbers of correctly detected fingerprint images of our method and other five methods in the test dataset from SPD2010.  
types, the training data scale, the labeling difficulty, the detection accuracy and the loss function. The analysis results are shown in Table 2 and it is explained as follows:  
1. The network number influences algorithm complexity and training complexity to some extent. SPBC and SP-Net has two networks to be trained, while SPOD and ours have only one.
2. For the network types, SPBC uses the VGG network and FCN network, SPOD modifies the Faster-RCNN network for detection, and SP-Net used the hourglass network and four layer regression network. In comparison, our SinNet is a customized network, which is newly designed for segmenting the fingerprint singular areas accurately.
3. The training data scale greatly affects the training complexity. Generally speaking, a good network can achieve excellent performance when the amount of data is limited. SPBC, SPOD use more than 10,000 fingerprint images and SP-Net use more than 1000 fingerprint images for training to achieve the effect described in their article, while our proposal just needs 290 fingerprint images. This proves the superiority of our proposed SinNet.
4. The labeling difficulty is affected by the training data scale and the way of labeling. For SPBC, its training data scale is large, and its way of labeling is a little complex. For SPOD, its training data scale is large, while its way of labeling is easy. For SP-Net, its training data scale is medium, while its way of labeling is hard. In comparison, our method has a smaller training data scale, and easier labeling. That is to say, the labeling difficulty in our method is the lowest, and it is convenient to apply.
5. The loss function plays an important role in the network training. Both SPBC and SP-Net have double loss functions for two networks and SPOD has double loss functions for anchor classification and coordinates regression. In comparison, our method needs only one mixed loss function for pixel classification.
6. The detailed detection results and analysis are shown on Section 4.4. Our method has indisputably achieved the best performance in terms of comprehensive indexes among the methods based on deep learning.  
From the above analysis, we can draw a conclusion that the properties of four methods based on deep learning are different and our proposed method has advantages in most aspects.