# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 3 Proposed Method
### Algorithm framework  
The whole flowchart of the proposed method is shown in Figure 1, with which the functions of the characteristic structures and modules are explained as follows:1. SinNet is the encoder-decoder architecture, and it has an encoder part and two corresponding decoder parts, followed by two final pixel-wise convolutional layers. The encoder-decoder architecture is a common architecture of semantic segmentation network, which can be used to segment target areas.
2. The encoder layers act as feature extractors, which aims at extracting target characteristics. The encoder layers include the convolution operation and the pooling operation. With the deepening of the number of layers, the semantic information extracted is advanced further.
3. The decoder layers are used to upsample the abstraction back into its original size in details. The decoder layers include the convolution operation and the upsampling operation. The final output image has the same size as the input image.
4. Five identical customized inception modules are adopted in the encoder part. Here, the inception module can expand the width of the network in structure and extract features on multiple scales in function, and its specific structure is shown in Figure 2. The inception module is to perform multiple convolution on input images in parallel, and stitch all output results into a very deep feature map, which aims to design a network with excellent local topology.
5. At the end of the encoder part, one batch normalization layer is used to prevent over-fitting, improve the robustness of the model to different hyper-parameters, and keep most activation functions away from its saturation region. It achieves accelerating network convergence speed and improving training stability.
6. Four skip connections are used for connecting the encoder part and decoder part, and they aim to let the final recovered feature maps fuse low-level features. It is worth noting that each connected feature map dimension should be consistent. The boundary of singular point area is fuzzy and the gradient is complex, so semantic segmentation needs more high resolution information. The skip connection could provide high resolution information for fusion.
7. At last, a simple and fast blob detection method called SimpleBlobDetector is adopted to locate the singular points.  
Figure 1: The flowchart of the proposed method.  
In order to further clarify our idea, the differences between fingerprint singular points and generic objects are presented in details. For the generic semantic segmentation task, he convolutional neural network, especially the encoder-decoder architecture network, usually needs to segment more than a dozen objects of different subject classes in natural images. Different objects in an image usually contain different features, such as texture, color, shape and size. However, the fingerprint images are simpler, and they are usually gray scale images. Their ridges are represented by dark lines and their valleys together with the background are represented by bright pixels. To some extent, the structures of singular points are simpler than generic objects so that we adopt a shallow encoder-decoder architecture network to achieve the singular point area segmentation. In order to enhance robustness for cores and deltas of different size, we design a wide network using an inception module for extracting multi-scale features. At the same time, we adopt the symmetrical structure and the skip connection to restore the details of fingerprint images.