# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 2 Related Work
Among the existing methods, this method has achieved the best result on the database SPD2010. [PERSON] et al. [14] propose a faster method by walking directly to the singular points, and the method does not need to visit each pixel or each small image block to locate the singular point. In [PERSON] et al. 's method, walking directional fields (WDFs) should be established at first, and then a walking strategy is easily carried out for fast location with an acceptable accuracy. Compared with methods based on the Poincare index and filter and model techniques, although these hybrid schemes can obtain better performance and higher efficiency in experimental evaluations, to a certain extent, they still depend on the quality of the fingerprint orientation field and does not work very well for low-quality fingerprint like the existing ones. In recent years, much attention has been given to using deep learning models for fingerprint recognition [21], such as segmentation [22; 23], orientation field estimation [24; 25], minutiae extraction [26; 27], and minutiae descriptor extraction [28]. [PERSON] et al. [15] firstly proposed the singular points detection method based on deep learning. They firstly design two convolutional neural networks in which one is used for classifying the core, the delta and the background and the other is used for estimating the positions of the core and the delta. Following this, a probabilistic method is used to determine the actual positions of singular points. However, the block classification method is somewhat rough, and this method has many processing steps. Its training process is also cumbersome including dividing the training images into many blocks and multi-scales. [PERSON] et al. [29] also used the idea of block classification to automatically detect fingerprint singular points. However, its performance does not surpass [PERSON] et al.'s. Inspired by the successful object detection in natural images, [PERSON] et al. [16] regard the singular point detection as the object detection problem and propose a deep learning detection algorithm via faster-RCNN. [PERSON] et al. 's algorithm has a two-step strategy: some candidate patches, in which it is probable for singular points to exist, are chosen by one network in the first step, and the precise singular points are selected and located from the candidate patches in the second step. Compared to [PERSON] et al. 's method, [PERSON] et al. 's is more precise in detection rate. However, for [PERSON] et al. 's method, the detection accuracy is still affected by anchor size, and low-quality small target detection is still a difficult problem [30]. That is to say, the faster-RCNN does not work very well for the fingerprint singular point detection, especially for the low-quality fingerprint. At the same time, it needs a large number of fingerprint images for training. In 2019, [PERSON] et al. [17] presented a macro-localization network and a micro-regression network to detect fingerprint singular points from coarse to fine, where the macro-localization network aims at extracting squares of size \(43\times 43\) pixels around singular points while the micro-regression network at regressing the accurate coordinates of singular points. They firstly adopt the idea of semantic segmentation for extracting fingerprint singular points. However, the semantic segmentation network (i.e., macro-localization network) just segments the big coarse image block containing singular points. For more precise positioning, all the segmented image blocks need to be put into the non-semantic segmentation network (i.e., micro-regression network) for training again. Actually, the idea of semantic segmentation plays a limited role in the whole method. Additionally, their twice training is time-consuming and laborious. What's more, the detection performance of [PERSON] et al. 's method, close to that of [PERSON] et al. 's method, is still unsatisfactory.