Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network

Cui, Zhe; Feng, Jianjiang; Zhou, Jie

doi:10.1109/tifs.2020.3017926

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Cui et al [ 24 ] propose an end-to-end network to directly output pixel-wise displacement field between two fingerprints, including a siamese network for feature embedding, and a following encoder-decoder network for regressing displacement field. The algorithm consists of two steps: minutiae-based coarse registration and CNN-based fine registration.…”

Section: Related Workmentioning

confidence: 99%

Fingerprint Recognition in Forensic Scenarios

Martins,

Silva,

Bernardino

2024

Sensors

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Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

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Section: Related Workmentioning

confidence: 99%

Fingerprint Recognition in Forensic Scenarios

Martins,

Silva,

Bernardino

2024

Sensors

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“…The limitation of the method is that the training fingerprints are relatively clean; thus it may not be effective in the case of low-quality images. Cui et al [27] proposed an attractive approach based on dense registration. The approach is a coarse-to-fine method containing two prime steps.…”

Section: Related Workmentioning

confidence: 99%

“…These new algorithms are much more accurate than traditional minutiaebased or ridge-based methods. With the development of convolutional neural networks (CNNs), some scholars have proposed approaches based on convolutional network techniques [24][25][26][27], improving accuracy with fewer constraints. Despite deep convolutional neural networks demonstrate its extraordinary power on various tasks.…”

Section: Introductionmentioning

confidence: 99%

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A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Bao,

Wang,

Gao

et al. 2023

IEEE Access

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With the widely used fingerprints to identify criminals, the influence of fingerprint deformation arouses attention in forensic science. To date, many approaches have been proposed to rectify distorted fingerprints. However, the performance in handling low-quality fingerprints extracted at a crime scene is less high quality than expected. This paper presents a combined method to rectify the latent fingerprints extracted at a crime scene. The method is a coarse-to-fine approach, combining the robustness of traditional pattern recognition and the accuracy of deep learning networks. We conducted several experiments to compare our approach with other approaches, including the nearest-neighbor search and network methods. The results show a remarkable improvement in fingerprint matching, especially in lowquality latent fingerprints. The top 25 cumulative match rate improves from 0.65 (original) to 1 (proposed method), whereas other approaches improve the result to 0.85 at best.

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“…Finger vein recognition is a biometric recognition technology that uses near-infrared light to collect finger vein images and identify them according to the extracted finger vein features [ 1 ]. Compared with common biometric features such as face [ 2 ] and fingerprint [ 3 ], the biggest advantage of finger vein feature is that it is located inside the human body. Moreover, due to the convenience of collection and quick identification of digital vein information, the identification can only be done in vivo, and it has a high level of safety [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

Wang

Liu

et al. 2022

Journal of Healthcare Engineering

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Region of interest (ROI) extraction is a key step in finger vein recognition preprocessing. The current method takes the finger region in the vein image as the ROI, but this method cannot obtain better recognition accuracy because it only removes the background noise of the image and ignores factors such as the position and shape of the finger. To solve this problem, we limited the ROI to a fixed region between two finger joint cavities, proposed a new large receptive field gradient operator, and designed and implemented a new method for ROI extraction. It uses a large receptive field to search the target, which is similar to human vision, thus solving the problem of difficult ROI localization for images with large gradient areas. Moreover, for external factors such as noise and uneven illumination in the vein image, the interference factors can be eliminated by averaging them to a larger range with a larger size operator to improve the accuracy of the subsequent matching recognition. To verify the effectiveness of the proposed method, we conducted sufficient matching experiments on three public finger vein datasets. On various datasets, our method significantly reduced the identified EER value, with the lowest EER value reaching 0.96%. The experimental results show that the proposed ROI extraction method can effectively eliminate the influence of finger position, finger shape, and other factors on the subsequent recognition performance, accurately locate the finger joint cavity, and effectively improve the recognition performance.

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Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network

Cited by 9 publications

References 26 publications

Fingerprint Recognition in Forensic Scenarios

Fingerprint Recognition in Forensic Scenarios

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

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