scholarly journals Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 786
Author(s):  
Ngoc Tuyen Le ◽  
Duc Huy Le ◽  
Jing-Wein Wang ◽  
Chih-Chiang Wang
Keyword(s):  
Clustering Algorithm ◽  
Adaptive Method ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Enhancement Method ◽  
Field Estimation ◽  
Verification Systems ◽  
Basic Features ◽  
Value Decomposition ◽  
Point Detection

Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.

scholarly journals A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection

2020 ◽  
Vol 10 (11) ◽  
pp. 3868
Author(s):  
Jiong Chen ◽  
Heng Zhao ◽  
Zhicheng Cao ◽  
Fei Guo ◽  
Liaojun Pang
Keyword(s):  
Singular Point ◽  
Semantic Segmentation ◽  
Singular Points ◽  
Detection Methods ◽  
Orientation Field ◽  
Global Features ◽  
Fingerprint Image ◽  
Detection Rates ◽  
Fingerprint Classification ◽  
Point Detection

As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.

scholarly journals Deep Learning for Fingerprint Localization in Indoor and Outdoor Environments

2020 ◽  
Vol 9 (4) ◽  
pp. 267 ◽  
Author(s):  
Da Li ◽  
Yingke Lei ◽  
Xin Li ◽  
Haichuan Zhang
Keyword(s):  
Magnetic Field ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Fingerprint Image ◽  
Density Peak ◽  
Localization Accuracy ◽  
Localization System ◽  
Outdoor Environments ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Wi-Fi and magnetic field fingerprinting-based localization have gained increased attention owing to their satisfactory accuracy and global availability. The common signal-based fingerprint localization deteriorates due to well-known signal fluctuations. In this paper, we proposed a Wi-Fi and magnetic field-based localization system based on deep learning. Owing to the low discernibility of magnetic field strength (MFS) in large areas, the unsupervised learning density peak clustering algorithm based on the comparison distance (CDPC) algorithm is first used to pick up several center points of MFS as the geotagged features to assist localization. Considering the state-of-the-art application of deep learning in image classification, we design a location fingerprint image using Wi-Fi and magnetic field fingerprints for localization. Localization is casted in a proposed deep residual network (Resnet) that is capable of learning key features from a massive fingerprint image database. To further enhance localization accuracy, by leveraging the prior information of the pre-trained Resnet coarse localizer, an MLP-based transfer learning fine localizer is introduced to fine-tune the coarse localizer. Additionally, we dynamically adjusted the learning rate (LR) and adopted several data enhancement methods to increase the robustness of our localization system. Experimental results show that the proposed system leads to satisfactory localization performance both in indoor and outdoor environments.

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