# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 2 Related Work  
Most researches on singular point detection are based on traditional digital image processing algorithms. Now, with its wide application in the field of computer vision, deep learning has provided new solutions to the singular point detection problem. From this point of view, the existing singular points detection methods can be mainly divided into two categories: (1) the methods with traditional digital image processing [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14], and (2) the ones with deep learning [15; 16; 17]. In the following, we shall give a thorough overview on detection methods. According to the principle, singular point detection methods on the traditional digital image processing can be further divided into three sub-categories, including methods on the Poincare index [3; 4; 5; 6; 7], ones on filter and model techniques [8; 9; 10; 11; 12] and ones on the hybrid schemes [13; 14]. The Poincare index of a point is defined as the cumulative orientation differences counterclockwise along a simple closed [3], and it can be used to judge whether there are singular points in an orientation field block or not. The Poincare index method is firstly proposed by [PERSON] et al. [4] and now is considered as the most classical way to detect singular points. Unfortunately, it does not work well for fingerprint local areas with unclear flow direction, which is its main drawback. To further improve detection performance, on the basis of the Poincare index method, some researchers add some global and local features to their methods [3; 5; 6; 7]. The most popular Poincare index method with the feature is presented by [PERSON] et al. [3], who proposed the orientation values along a circle (DORIC) feature for the removal of spurious singular points and then used the classic Poincare index method [4] to detect the singular points initially. As a novel constraint, a core-delta relation is utilized to select singular points finally. Although the Poincare index methods have achieved, to some extent, satisfactory detection results for high-quality fingerprint, there are still limitations. For example, most of these methods require the high-precision fingerprint orientation field before detection, which means that the detection result is very dependent on the quality of the original image and the performance fingerprint preprocessing algorithm. The filter and model techniques provide another way for detecting fingerprint singular points. Methods based on filter and model techniques detect cores and deltas generally by means of using the designed corresponding filters or analyzing the corresponding models, such as complex filter methods [8; 9], multi-scale Gaussian filter method [10] and multi-scale orientation entropy method [11]. The most popular one is presented by [PERSON] et al. [12], and it combined zero-pole model and Hough transform (HT) to detect the core and the delta. The orientation field is estimated by Zero-pole Model. HT is used for coarse detection at the global level and the Poincare index is used for fine detection. Besides [PERSON] et al. 's method, there are a certain number of similar methods, most of which can obtain better performance in some experimental evaluations. However, it is worth noting that most of them need to visit every pixel or block to extract enough information to detect singular points [14], which is very time-consuming. At the same time, they cannot perform very well on low-quality fingerprint either. The emergence of hybrid schemes makes it possible to achieve higher detection rate and faster detection speed. [PERSON] et al. [13] develop a new hybrid index of singular points based on the definition of angle matching index (AMI) in vector fields. AMI is a specific polynomial model of orientation field, which could be also used in other areas, especially the vector field analysis. The AMI information of candidate singular points is collected and the conventional convergence index filter framework is modified.