Filter Design Based on Spectral Dictionary for Latent Fingerprint Pre-enhancement

Chaidee, Watcharapong; Horapong, Kittipol; Areekul, Vutipong

doi:10.1109/icb2018.2018.00015

Cited by 11 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence we only manipulate the spectral magnitude image. Our previous work proposed a dictionary learning in the frequency domain, called spectral dictionary [13]. The enhanced results of this approach are auspicious.…”

Section: A Research Motivationmentioning

confidence: 99%

“…Xu et al [12] combined Gabor dictionaries with minutiae dictionaries. On the contrary, Chaidee et al [13] designed the dictionary learning from Gabor spectral responses via Gabor filter banks and sparse representation in the frequency domain. Instead of using the Gabor filter, the dictionary, called SpectralDict, predicted the shape of the filter in the frequency domain for enhancing latent fingerprints.…”

Section: A Dictionary Learning Approachmentioning

confidence: 99%

“…If the is greater than or equal to the threshold ( ≥ 0.67), we enhance this block using the matched filter (8). Otherwise, we use the ideal bandpass filter in (13) to keep the fingerprint spectrum for the next tier. We enhance all four blocks in parallel.…”

Section: ) Iterative Blocks Enhancement and Feedbackmentioning

confidence: 99%

“…Currently, we can compare with only seven published algorithms due to available latent fingerprint enhancement results from this NIST-SD27 database. We obtained the enhanced results from authors of GlobalDict [6], LocalDict [7], RidgeDict [8], SpectralDict [13], DN-Unets [18], and Semi-Prgs [26]. In addition, we used the released code available in [45] to enhance latent fingerprint images for the FingerNet algorithm [14].…”

Section: A Benchmark Tests On Nist-sd27 Datasetmentioning

confidence: 99%

“…Several latent fingerprint enhancement algorithms have emerged from machine learning tools. The most popular approaches are based on dictionary learning [6]- [13] and deep learning [14]- [22]. The key of these learning approaches is to learn from a large set of high-quality fingerprint images and to use these learning models to enhance friction ridges of lowquality latent fingerprint images.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: A Research Motivationmentioning

confidence: 99%

Section: A Dictionary Learning Approachmentioning

confidence: 99%

Section: ) Iterative Blocks Enhancement and Feedbackmentioning

confidence: 99%

Section: A Benchmark Tests On Nist-sd27 Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2021

Self Cite

View full text Add to dashboard Cite

show abstract

Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

Joshi,

Prakash,

Kumar

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Spectral Filter Predictor for Progressive Latent Fingerprint Restoration

Kriangkhajorn,

Horapong,

Areekul

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Filter Design Based on Spectral Dictionary for Latent Fingerprint Pre-enhancement

Cited by 11 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

Unsupervised domain alignment of fingerprint denoising models using pseudo annotations

Spectral Filter Predictor for Progressive Latent Fingerprint Restoration

Contact Info

Product

Resources

About