Multisensor Optical and Latent Fingerprint Database

Sankaran, Anush; Vatsa, Mayank; Singh, Richa

doi:10.1109/access.2015.2428631

Cited by 69 publications

(51 citation statements)

References 23 publications

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“…To evaluate the performance of the proposed latent fingerprint reconstruction technique, we conducted three different experiments on publicly available datasets of latent fingerprints. Unfortunately, NIST-SD27 [6] is no longer available, so the IIIT-Delhi Latent fingerprint [20] and IIIT-Delhi Multi Sensor Latent Fingerprint (MOLF) [23] databases were used to evaluate the proposed method. In all experiments, we used VeriFinger 7.0 SDK [18] and the NIST Biometrics Image Software (NBIS) [31] to match the reconstructed samples.…”

Section: Methodsmentioning

confidence: 99%

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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Section: Methodsmentioning

confidence: 99%

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)

View full text Add to dashboard Cite

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“…The experiments were carried out using publicly available datasets. Latent fingerprint enhancement was evaluated using the IIIT-Delhi Latent fingerprint [30] and IIIT-Delhi MOLF [31] datasets.…”

Section: Methodsmentioning

confidence: 99%

“…It is therefore possible to match latent fingerprints to those acquired by a sensor. Following the testing protocol by Sankaran et al [31], we considered the first and second instance fingerprints for each user from a sensor scanned database as the gallery. The whole latent fingerprint database consisting of 4400 samples was considered as probe set.…”

Section: Methodsmentioning

confidence: 99%

“…For this experiment, we refer to the case from [31], where minutiae were extracted automatically and afterwards matched using one of the standard algorithms. Sankaran et al evaluated the performance of publicly available NBIS and commercial VeriFinger [26] fingerprint matching methods, reporting very poor performance for both.…”

Section: Methodsmentioning

confidence: 99%

“…We evaluate our method on two publicly available latent fingerprint datasets: IIIT-Delhi latent fingerprint [30] (for latent-to-latent fingerprints matching) and IIIT-Delhi MOLF [31] (for latent-to-sensor-scan fingerprints matching). We show the broad applicability of our approach and discuss the possible future directions.…”

Section: Introductionmentioning

confidence: 99%

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Generative convolutional networks for latent fingerprint reconstruction

Svoboda

Monti

Bronstein

2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature extraction methods such as MINDTCT, followed by biometric comparison using MCC and BO-ZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors.

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