U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

Ch, Ramakrishna Prabhu; Yu, Xiao-Jing; Wang, Zhangyang; Liu, Ding; Jiang, Anxiao

doi:10.1007/978-3-030-25614-2_3

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…Compare with others In order to better illustrate the superiority of our algorithm, we chose several open-source methods for comparison. Since the method based on the traditional direction field is not open source, so we choose the deep learning methods developed rapidly in recent years, U-finger [19] and FPD-M-net [31]. They are both convolutional network models.…”

Section: Evaluation Of Fingerprin Restorationmentioning

confidence: 99%

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Fingerprint restoration using cubic Bezier curve

Yao

et al. 2020

BMC Bioinformatics

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Background Fingerprint biometrics play an essential role in authentication. It remains a challenge to match fingerprints with the minutiae or ridges missing. Many fingerprints failed to match their targets due to the incompleteness. Result In this work, we modeled the fingerprints with Bezier curves and proposed a novel algorithm to detect and restore fragmented ridges in incomplete fingerprints. In the proposed model, the Bezier curves’ control points represent the fingerprint fragments, reducing the data size by 89% compared to image representations. The representation is lossless as the restoration from the control points fully recovering the image. Our algorithm can effectively restore incomplete fingerprints. In the SFinGe synthetic dataset, the fingerprint image matching score increased by an average of 39.54%, the ERR (equal error rate) is 4.59%, and the FMR1000 (false match rate) is 2.83%, these are lower than 6.56% (ERR) and 5.93% (FMR1000) before restoration. In FVC2004 DB1 real fingerprint dataset, the average matching score increased by 13.22%. The ERR reduced from 8.46% before restoration to 7.23%, and the FMR1000 reduced from 20.58 to 18.01%. Moreover, We assessed the proposed algorithm against FDP-M-net and U-finger in SFinGe synthetic dataset, where FDP-M-net and U-finger are both convolutional neural network models. The results show that the average match score improvement ratio of FDP-M-net is 1.39%, U-finger is 14.62%, both of which are lower than 39.54%, yielded by our algorithm. Conclusions Experimental results show that the proposed algorithm can successfully repair and reconstruct ridges in single or multiple damaged regions of incomplete fingerprint images, and hence improve the accuracy of fingerprint matching.

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Section: Evaluation Of Fingerprin Restorationmentioning

confidence: 99%

“…Incompleteness is one of the most significant features of low-quality fingerprints. There are some methods based on the directional field model, while others are based on deep learning [19][20][21] to try to repair incomplete fingerprint images. The performance of incomplete fingerprint recognition depends mainly on the effect of fingerprint image enhancement.…”

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confidence: 99%

Fingerprint restoration using cubic Bezier curve

Yao

et al. 2020

BMC Bioinformatics

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“…To reduce human involvement, some level of automation was introduced in the fingerprint identification process. Automatic ROI cropping (Choi et al, 2012 ; Zhang et al, 2013 ; Cao et al, 2014 ; Nguyen et al, 2018a ), ridge-flow estimation (Feng et al, 2013 ; Cao et al, 2014 , 2015 ; Yang et al, 2014 ), and ridge-enhancement (Feng et al, 2013 ; Li et al, 2018 ; Prabhu et al, 2018 ) methods were proposed by various researchers. The Descriptor-Based Hough Transform (DBHT) (Paulino et al, 2013 ) was proposed to align and match the fingerprints.…”

Section: Literature Surveymentioning

confidence: 99%

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Deshpande¹,

Malemath

Patil

et al. 2020

Front. Robot. AI

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Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

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“…Yet it remains questionable whether restoration-based approaches would actually boost the visual understanding performance, as the restoration/enhancement step is not optimized towards the target task and may bring in misleading information and artifacts too. For example, a recent line of researches [8,[39][40][41][42][43][44][45][46][47][48] discuss on the intrinsic interplay relationship of low-level vision and high-level recognition/detection tasks, showing that their goals are not always aligned.…”

Section: Introductionmentioning

confidence: 99%

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Yang

Yuan

Ren

et al. 2020

IEEE Trans. on Image Process.

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163

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Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

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U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

Cited by 15 publications

References 17 publications

Fingerprint restoration using cubic Bezier curve

Fingerprint restoration using cubic Bezier curve

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

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