Progressive Latent Fingerprint Enhancement Using Two-Stage Spectrum Boosting with Matched Filter and Sparse Autoencoder

Horapong, Kittipol; Srisutheenon, Kittinuth; Areekul, Vutipong

doi:10.1109/ecti-con49241.2020.9158210

Cited by 4 publications

(12 citation statements)

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“…If the algorithm fails to enhance the genuine friction ridges, the enhanced error propagates to the nearby blocks resulting in a domino effect. In another preliminary work [27], we have tried to combine two concepts. We employed an autoencoder to predict a corresponding matched filter with the progressive feedback mechanism.…”

Section: A Research Motivationmentioning

confidence: 99%

“…The drawback of this method is that the highest quality block was manually selected by human assistance. Horapong et al [27] combined the progressive feedback method [26] with a learning model in a two-stage design. The first stage iteratively applied a matched filter in the high-quality fingerprint region.…”

Section: Progressive Feedback Approachmentioning

confidence: 99%

“…In the design, we need to smooth the matched filter to increase the filter's bandwidth by convolving with a Gaussian smoothing filter with a standard deviation of 2.75, as in [26], [27]. Finally, the magnitude of the matched filter is given by Finally, we take a 128128 inverse fast Fourier transform onto the spectral boosted magnitude with the original phase of its corresponding spectral patch, as shown in (10).…”

Section: ) Initial Block Enhancementmentioning

confidence: 99%

“…We use the same measure as our preliminary works [26], [27]. The spectral peak ratio (SPR) is defined by Green area shows the enhanced blocks using matched filtering on ̂( , ).…”

Section: ) Iterative Blocks Enhancement and Feedbackmentioning

confidence: 99%

“…In this benchmark test, we obtained only enhanced results from three published algorithms; SpectralDict [13], FingerNet [14], and 2-Stage-Prgs [27]. Unfortunately, the state-of-the-art authors [18] could not provide us with the enhanced results in this database.…”

Section: B Benchmark Tests On Iitd-molf Datasetmentioning

confidence: 99%

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Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

2021

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The objective of this research is to design an efficient algorithm that can successfully enhance a targeted latent fingerprint from various complex backgrounds under an uncontrolled environment. Most algorithms in literature exploited dictionary learning schemes and deep learning architectures to capture latent fingerprints from complicated backgrounds and noise. However, an algorithm learned from other high-quality fingerprint images may not solve all possible cases within a given unseen image. We propose a new feedback framework to distinguish latent fingerprints from complex backgrounds and gradually improve friction-ridge quality using the information provided inside a given unseen image. We combine two efficient mechanisms. The first mechanism enhances high-quality areas in priority and feeds the enhanced areas back to improve the quality of latent fingerprints in the nearby area. The second mechanism is to verify that the first mechanism works correctly by detecting anomalously enhanced fingerprint patterns. The second mechanism employs a spectral autoencoder that learns from good fingerprint spectra in the frequency domain. The anomalous fingerprint area is sent back to the first mechanism for further improving the enhanced result. We benchmark the proposed algorithm against available state-of-the-art algorithms using two fingerprint matching systems (one commercial off-the-shelf and one open-source) on two public latent fingerprint databases. The experimental results show that the proposed algorithm outperforms most state-of-the-art algorithms in the literature.

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Section: A Research Motivationmentioning

confidence: 99%

Section: Progressive Feedback Approachmentioning

confidence: 99%

Section: ) Initial Block Enhancementmentioning

confidence: 99%

“…We use the same measure as our preliminary works [26], [27]. The spectral peak ratio (SPR) is defined by Green area shows the enhanced blocks using matched filtering on ̂( , ).…”

Section: ) Iterative Blocks Enhancement and Feedbackmentioning

confidence: 99%

Section: B Benchmark Tests On Iitd-molf Datasetmentioning

confidence: 99%

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Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

2021

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Spectral Filter Predictor for Progressive Latent Fingerprint Restoration

Kriangkhajorn,

Horapong,

Areekul

2024

IEEE Access

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Latent fingerprint restoration is one of the most challenging problems for fingerprint identification. According to the current next-generation identification (NGI) system fact sheet, the number of unsolved latent fingerprint cases is more than a million and is increasing daily. The current state-of-the-art algorithms utilize various deep-learning networks to restore latent fingerprints in the spatial domain. Unfortunately, these algorithms faced difficulty recovering genuine friction ridges from low-quality latent fingerprints with complex backgrounds and noise. Instead of using deep-learning models to restore latent fingerprints directly in the spatial domain, we used the deep-learning model to predict a frequency-domain filter for restoring each latent input partitioning block. We integrated this new filter predictor model within a progressive feedback framework. As a result, the proposed method can reveal genuine friction ridges from complex backgrounds, especially around very low-quality areas and singular point areas, which is crucial to the success of latent fingerprint identification. We benchmarked our proposed method against state-of-theart methods with four public latent fingerprint datasets. In addition, we used one commercial-of-the-shelf (COTS) and another open-source fingerprint matchers in our benchmark. The experiments showed that the proposed method outperformed current state-of-the-art methods in identification accuracy. Compared to the state-of-the-art methods using deep learning in the spatial domain, the proposed method achieved an average rank-1 identification accuracy improvement of 3.75% and 3.19% of four public datasets with the COTS and open-source fingerprint matchers, respectively. Finally, the execution code was made publicly available for non-commercial purposes at https://github.com/skconan/SFP-Progressive-Feedback-Latent-Fingerprint-Restoration.

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FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement

Zhu

Yin

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Latent fingerprint enhancement is an essential preprocessing step for latent fingerprint identification. Most latent fingerprint enhancement methods try to restore corrupted gray ridges/valleys. In this paper, we propose a new method that formulates latent fingerprint enhancement as a constrained fingerprint generation problem within a generative adversarial network (GAN) framework. We name the proposed network FingerGAN. It can enforce its generated fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the corresponding ground truth instance in terms of the fingerprint skeleton map weighted by minutia locations and the orientation field regularized by the FOMFE model. Because minutia is the primary feature for fingerprint recognition and minutia can be retrieved directly from the fingerprint skeleton map, we offer a holistic framework that can perform latent fingerprint enhancement in the context of directly optimizing minutia information. This will help improve latent fingerprint identification performance significantly. Experimental results on two public latent fingerprint databases demonstrate that our method outperforms the state of the arts significantly. The codes will be available for non-commercial purposes from https://github.com/HubYZ/LatentEnhancement.

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Progressive Latent Fingerprint Enhancement Using Two-Stage Spectrum Boosting with Matched Filter and Sparse Autoencoder

Cited by 4 publications

References 12 publications

Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

Spectral Filter Predictor for Progressive Latent Fingerprint Restoration

FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement

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