Deep learning‐based approach to latent overlapped fingerprints mask segmentation

Stojanović, Branka; Marques, Oge; Nešković, Aleksandar N.

doi:10.1049/iet-ipr.2017.1227

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…Deep learning can omnidirectionally percept and memorize the image features so as to precisely classify (or segment) the images if there are a large number of learning samples and sample selection is reasonable. [102][103][104][105][106][107][108][109][110] In the cases of a large sample of the images obtained, combination of the two expects to generate good and precise methods for sonar image segmentation. (8) The wavelet packet transform is subjected to analysis high-frequency components of the images.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A review on the wavelet methods for sonar image segmentation

Tian

Lan

Guo

2020

International Journal of Advanced Robotic Systems

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The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A review on the wavelet methods for sonar image segmentation

Tian

Lan

Guo

2020

International Journal of Advanced Robotic Systems

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“…However, extracting the ROI from overlapping regions of multiple fingerprint images is a challenging problem to be solved urgently in constructing rolled fingerprints. In [10], a block segmentation method was proposed to extract the overlapped regions of two latent fingerprints. Moreover, it proposed a novel idea to judge the source of image blocks based on their feature information.…”

Section: B Image Block Segmentationmentioning

confidence: 99%

BlockRFC: Real-Time Rolled Fingerprint Construction and Distortion Rectification

Zhang

Ming-hua

et al. 2020

IEEE Access

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Compared with a flat fingerprint, the rolled fingerprint has a larger fingerprint area and can be extracted more minutiae. It has high requirements in many fields, not only in the military environment or the police field but also in many civil application fields. The challenge that has been troubled for a long time is that contact-based rolled fingerprint registration is easy to cause obvious distortion without human experts' supervision, which has a negative impact on fingerprint recognition performance. Due to the elastic deformation of fingertips, the mosaicking gaps in the rolled fingerprint are usually visible but challenging to locate. To address these problems, we propose a novel rolled fingerprint construction algorithm called BlockRFC (Block-based Rolled Fingerprint Construction) in this paper. BlockRFC's core idea is to use the fingerprint image block as a processing unit for mosaicking and distortion rectification. BlockRFC is based on a real-time mosaicking framework, which makes it possible to construct a rolled fingerprint while collecting fingerprint images. One distinctive advantage of BlockRFC is that it does not require minutiae or ridge information, but fully utilizes the gray-scale information and foreground area in the fingerprint image block. In this paper, we first propose a metric called Mosaicking Gap Rate (MGR), which can effectively quantify the mosaicking gaps in the rolled fingerprints. Experimental results show that the proposed method can not only effectively locate and eliminate the mosaicking gaps but also have better recognition accuracy than previous methods.

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“…At the same time, it may cause some damage to the handprints during use [12]. Stojanovic, B et al proposed a new overlapping fingerprint mask segmentation method using a convolutional neural network in machine learning, where latent handprints are preprocessed and associated with an ideal model of papillary stripe lines and separated based on the strength of the association [13]. Hsieh-Chang, H et al addressed computerized handprint separation methods, and they, by means of a recursive correction algorithm and a constrained slack labeling algorithm.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Optical Separation of Overlapping Handprints

2023

Applied Mathematics and Nonlinear Sciences

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Since overlapping handprints often have the potential to become a breakthrough point in criminal cases, extracting and separating overlapping handprints at the scene has become an urgent problem to be solved in criminal technical examination. In this context, this paper proposes a machine learning-based optical separation method for overlapping handprints, which uses SVM to simplify the classification of the features of the training samples and completes the measurement of the features based on the statistical properties of the image grayscale histogram. Using image blind source separation, independent component analysis and pixel iteration algorithm to separate the overlapped image to get two different spatial resolution images, SSIM is chosen to evaluate the quality of the separated image. Then, the target classification recognition and extraction techniques of the method in the paper are analyzed by overlapping handprint optical separation experiments. The results show that compared with the traditional algorithms, the TPR, FPR, overlap degree and accuracy of this paper’s method in dealing with overlapping handprint images with deep fuzzy features are at a minimum of 0.9258, 0.3723, 0.4645, 0.9572, which are all greater than several other algorithms, which proves that the overlapping handprint optical separation method proposed in this paper based on machine learning has obvious advantages.

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Deep learning‐based approach to latent overlapped fingerprints mask segmentation

Cited by 8 publications

References 30 publications

A review on the wavelet methods for sonar image segmentation

A review on the wavelet methods for sonar image segmentation

BlockRFC: Real-Time Rolled Fingerprint Construction and Distortion Rectification

Machine Learning Based Optical Separation of Overlapping Handprints

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