Generative convolutional networks for latent fingerprint reconstruction

Svoboda, Jan; Monti, Federico; Bronstein, Michael M.

doi:10.1109/btas.2017.8272727

Cited by 33 publications

(15 citation statements)

References 19 publications

(38 reference statements)

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“…The network was trained over 40 epochs, each epoch consisting of 6,265 iterations with a batch size = 64. Adam optimization method [17,28] is used as the optimizer due to its fast convergence with beta = 0.5 and learning rate = 10 −4 .…”

Section: Time (Sec) Methodsmentioning

confidence: 99%

Fingerprint Distortion Rectification Using Deep Convolutional Neural Networks

Dabouei

Kazemi²,

Iranmanesh³

et al. 2018

2018 International Conference on Biometrics (ICB)

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Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems. This negative effect brings inconvenience to users in authentication applications. However, in the negative recognition scenario where users may intentionally distort their fingerprints, this can be a serious problem since distortion will prevent recognition system from identifying malicious users. Current methods aimed at addressing this problem still have limitations. They are often not accurate because they estimate distortion parameters based on the ridge frequency map and orientation map of input samples, which are not reliable due to distortion. Secondly, they are not efficient and requiring significant computation time to rectify samples. In this paper, we develop a rectification model based on a Deep Convolutional Neural Network (DCNN) to accurately estimate distortion parameters from the input image. Using a comprehensive database of synthetic distorted samples, the DCNN learns to accurately estimate distortion bases ten times faster than the dictionary search methods used in the previous approaches. Evaluating the proposed method on public databases of distorted samples shows that it can significantly improve the matching performance of distorted samples.

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Section: Time (Sec) Methodsmentioning

confidence: 99%

Fingerprint Distortion Rectification Using Deep Convolutional Neural Networks

Dabouei

Kazemi²,

Iranmanesh³

et al. 2018

2018 International Conference on Biometrics (ICB)

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“…NFIQ assigns each fingerprint a numerical score from 1 (high quality) to 5 (low quality). Quality scores are computed for the reconstructed samples by our method and compared to score of both the raw latent fingerprints and those enhanced by the generative model developed by Svoboda et al [29]. Figure 8 shows the quality scores of the reconstructed samples, and Fig.…”

Section: Quality Of the Reconstructed Fingerprintsmentioning

confidence: 99%

“…Compared to other biometric traits, such as iris, the fingerprint has a unique superiority of being Figure 1. Examples of different latent fingerprint reconstruction methods: a) a latent fingerprint with severe distortion and missing area, b) minutiae-based prediction using [24], c) ridge-based reconstruction using [29], d) constrained ridge-based reconstruction using the proposed algorithm. collected indirectly from crime scenes from latent friction ridge impressions.…”

Section: Introductionmentioning

confidence: 99%

“…In the ridge-base methods [29,35,33,34,5], algorithms try to predict the ridge information, which can be the orientation map or the ridge pattern itself, and minimize the similarity between the generated information and their ground truths. Then minutiae information can be extracted from the predicted ridge information.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1(c) shows a reconstructed ridge map for a latent fingerprint with severe distortion. As the reconstructed ridge map indicates, algorithm [29] generates meaningful ridge information for parts that contain some ridge information in the input latent sample, but for other parts it produces random ridge patterns which drastically decreases the matching score.…”

Section: Introductionmentioning

confidence: 99%

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ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

Dabouei

Soleymani

Kazemi

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latent fingerprint reconstruction model based on conditional generative adversarial networks (cGANs). Two modifications are applied to the cGAN to adapt it for the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual ID preservation approach is developed to force the generator to preserve the ID information during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. We achieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50 accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.

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FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks

Adiga

Sivaswamy

2019

The Springer Series on Challenges in Machine Learning

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Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extraction of the fingerprint from a degraded image requires denoising and inpainting. We propose to address these problems with an endto-end trainable Convolutional Neural Network based architecture called FPD-M-net, by posing the fingerprint denoising and inpainting problem as a segmentation (foreground) task. Our architecture is based on the M-net with a change: structure similarity loss function, used for better extraction of the fingerprint from the noisy background. Our method outperforms the baseline method and achieves an overall 3rd rank in the Chalearn LAP Inpainting Competition Track 3−Fingerprint Denoising and Inpainting, ECCV 2018.

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Generative convolutional networks for latent fingerprint reconstruction

Cited by 33 publications

References 19 publications

Fingerprint Distortion Rectification Using Deep Convolutional Neural Networks

Fingerprint Distortion Rectification Using Deep Convolutional Neural Networks

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks

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