# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 1 Introduction  
Fingerprint recognition was originally used for criminal investigation and has gradually been extended to other applications such as border control, computer logon, mobile payment, and so on [1]. The singular point is one of the most important and obvious global features for fingerprints, and it plays an essential role in fingerprint registration, fingerprint classification, fingerprint indexing and fingerprint template protection. Image processing and analysis of the fingerprint are typically based on the location and pattern of the singular points in the fingerprint images. The singular point area is defined as a region where the ridge curvature is higher than normal or the direction of the ridge changes rapidly [2]. Generally speaking, the singular points (both cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and influence the distribution of orientation field largely [3]. Better singular point detection is one of the most important challenges in the field of fingerprint recognition, especially for the low-quality fingerprint image. Singular points detection has been studied for many years and lots of excellent methods have been proposed based on traditional digital image processing technology, such as the Poincare index [3; 4; 5; 6; 7], the filter and model techniques [8; 9; 10; 11; 12] and the hybrid schemes [13; 14]. These methods have achieved, to some extent, satisfactory detection results for high-quality fingerprint. However, most of them require a high-precision fingerprint orientation field for detection, meaning that the detection resultis very dependent on the performance of fingerprint preprocessing algorithm, which is indirect and time-consuming. In recent years, singular point detection based on deep learning has come up [15; 16; 17], and its advantage over the traditional methods lies in that the original fingerprint images are directly adopted instead of the orientation fields. It is worth mentioning that [PERSON] et al. [17] firstly adopt the idea of semantic segmentation for extracting fingerprint singular points. However, their 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 their non-semantic segmentation network (i.e., micro-regression network) for training again. In summary, most methods based on deep learning need too much training data or complicated training process, which are inefficient for practical applications. More importantly, in terms of detection rates, neither methods based on traditional digital image processing technology [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14] nor those on deep learning [15; 16; 17], are satisfactory, especially for the low-quality fingerprint. Motivated by these concerns, we try to design a higher performance detection method which has simple and effective network architecture and requires a smaller amount of training data. Semantic segmentation, i.e., making a prediction at every pixel, is the natural next step for whole-image classification, object detection, part and key point prediction as well as local correspondence [18]. As long as the detected singular points are located in the accurate singular point areas, the detection result is determined to be correct. Therefore, in this paper, we treat singular points detection as a problem of semantic segmentation of small singular point area completely and directly and thus propose a new customized network architecture named SinNet, which contains a symmetrical encoder-decoder network with inception modules [19], skip connections [20] and batch normalization. The overall work uses SinNet, a pixel-to-pixel system, to extract the accurate small singular point area directly, and followingly, uses a simple post-processing without training to locate the singular point quickly. The contributions of this study are mainly two-fold:  
1.