# A Method for Singular Points Detection Based on Faster-RCNN
## 1 Introduction  
Of all biological characteristics, fingerprints have become one of the most describable biometric traits and used for biometric verification and identification tasks [1]. Nevertheless, with explosive growth in fingerprint databases (e.g., ID Card fingerprints), automatic recognition system has become increasingly more difficult than ever. As a global feature of fingerprint, singular points (shown in Figure 1) [1] could be used in fingerprint pattern classification [2], which could reduce the matched templates so as to reduce matching time in large databases during identification tasks. Moreover, since the fingerprint alignment according to minutiae is usually not so satisfactory, singular points are used as reference points for alignment in fingerprint minutiae matching [3]. In [4], a robust fingerprint matching system is proposed by extracting region of interest (ROI) centered at the core points and align two fingerprints by maximizing their orientation correlation. In [5], [PERSON] et al. proposed a Poincare index approach to obtain singular regions for fingerprint classification. By calculating the cumulative changes along a counter-clockwise closed contour in fingerprint orientation field, [PERSON] et al. [6] proposed an improved method based on Poincare index to judge whether there exist singular points. However, almost the methods based on Poincare index are fully depended on fingerprint orientation field Then, algorithms are affected by noise easily. To solve this problem, the Zero-pole Model and Hough Transform (HT) are combined to detect singular points in [7] and the Poincare index is used to refine positions of the candidate singular points. Contrary to orientation field generation, detection of singular points is simplified to determinethe parameters of the Zero-pole Model. Obviously, the performance of these conventional algorithms for singular points detection is limited by the accuracy of the Poincare index. Nowadays, the progress on the development of deep convolutional neural networks (CNNs) [8] has significantly advanced the state-of-the-art performance on a wide variety of computer vision tasks (e.g., face recognition [9; 10], video recognition [11]). In object detection tasks, Faster-RCNN [12] obtained an excellent performance and a Region Proposal Network (RPN) was introduced to simultaneously predict object bounds and corresponding scores at each position. Finally, locations of objects could be determined according to corresponding scores. Furthermore, [PERSON] et al. [13] proposed a novel approach for palmprint verification by using deep region of interest (ROI) and feature extraction models. Due to the outstanding learning behavior, CNNs are also used in fingerprint fields. [PERSON] et al. [14] achieved an outstanding effect in fingerprint classification tasks based on the depth neural network method. In [15], deep learning technique also shows good performance in partial fingerprint matching. [PERSON] et al. [16] proposed a two-step CNN method to extract the coordinates of the sweat pores from fingerprint images, which perform first the detection of candidate regions and Then, the extraction of the points of interest in the second step. Besides, [PERSON] et al. [17] used fully convolutional networks (FCN) [18] to detect fingerprint singular points. Nevertheless, this method only predict location information and obtain poor performance on some specific fingerprint images, such as the issue that two core points are very close. In this study, we propose a new method for singular points detection based on Faster-RCNN [12] and an orientation constraint is introduced to obtain orientation information of singular points, which is usually used for fingerprint alignment. Besides, since not all fingerprint images have delta points, on the other hand, fingerprint alignment can be achieved only by orientations of up-core points. Then, we only estimate that of up-core points.