A Fingerprint Verification Algorithm Using the Smallest Minimum Sum of Closest Euclidean Distance

Bhowmik, Ujjal Kumar; Ashrafi, Ashkan; Adhami, Reza R.

doi:10.1109/conielecomp.2009.57

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…To quantify the similarity between the sample and the control, the Euclidean Distance Formula is used to calculate the similarity between fingerprint characteristic matrixes using eq . ,,,, In the collected values, ( z i 1 , z i 2 ) and ( x i 1 , x i 2 ) are the feature point coordinates and ( a , b ) is the coordinate of the image core point. When the matching score P equals to 1, these two matrixes are exactly the same.…”

Section: Resultsmentioning

confidence: 99%

Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence Program

Dong

Niu

Zhang

et al. 2020

ACS Appl. Mater. Interfaces

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Development and comparison of the latent fingerprints (LFPs) are two major studies in detection and identification of LFPs, respectively. However, integrated research studies on both fluorescent materials for LFP development and digital-processing programs for LFP comparison are scarcely seen in the literature. In this work, highly efficient red-emissive carbon dots (R-CDs) are synthesized in one pot and mixed with starch to form R-CDs/starch phosphors. Such phosphors are comparable with various substrates and suitable for the typical powder dusting method to develop LFPs. The fluorescence images of the developed LFPs are handled with an artificial intelligence program. For the optimal sample, this program presents an excellent matching score of 93%, indicating that the developed sample has very high similarity with the standard control. Our results are significantly better than the benchmark obtained by the traditional method, and thus, both the R-CDs/starch phosphors and the digital processing program fit well for the practical applications.

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Section: Resultsmentioning

confidence: 99%

Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence Program

Dong

Niu

Zhang

et al. 2020

ACS Appl. Mater. Interfaces

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show abstract

“…Hereafter, the Euclidean Distance Formula is used to compute the similarity of fingerprint characteristic matrices using Eq 2 to measure the similarity between the samples and the control [ 11 , 60 – 62 ]. The coordinates of the feature points in the collected values are (z i1 , z i2 ), (x i1 , x i2 ), and the coordinate of the image core point represented by (a, b).…”

Section: Mechanism Insight Of Synthesis Of N-s@mcds From Marigold Ext...mentioning

confidence: 99%

Harnessing fluorescent carbon quantum dots from natural resource for advancing sweat latent fingerprint recognition with machine learning algorithms for enhanced human identification

Yadav,

Mudgal,

Mishra

et al. 2024

PLoS ONE

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Nowadays, it is fascinating to engineer waste biomass into functional valuable nanomaterials. We investigate the production of hetero-atom doped carbon quantum dots (N-S@MCDs) to address the adaptability constraint in green precursors concerning the contents of the green precursors i.e., Tagetes erecta (marigold extract). The successful formation of N-S@MCDs as described has been validated by distinct analytical characterizations. As synthesized N-S@MCDs successfully incorporated on corn-starch powder, providing a nano-carbogenic fingerprint powder composition (N-S@MCDs/corn-starch phosphors). N-S@MCDs imparts astounding color-tunability which enables highly fluorescent fingerprint pattern developed on different non-porous surfaces along with immediate visual enhancement under UV-light, revealing a bright sharp fingerprint, along with long-time preservation of developed fingerprints. The creation and comparison of latent fingerprints (LFPs) are two key research in the recognition and detection of LFPs, respectively. In this work, developed fingerprints are regulated with an artificial intelligence program. The optimum sample has a very high degree of similarity with the standard control, as shown by the program’s good matching score (86.94%) for the optimal sample. Hence, our results far outperform the benchmark attained using the conventional method, making the N-S@MCDs/corn-starch phosphors and the digital processing program suitable for use in real-world scenarios.

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“…In the paper by Bhowmik et al [2], a Euclidean distance based minutia matching algorithm was proposed to further improve the matching accuracy in fingerprint verification system. This was done by extracting the matched minutia pairs from both test and template datasets using the smallest minimum sum of closest Euclidean distance (SMSCED) corresponding with the rotation angle and chosen threshold values.…”

Section: Related Studiesmentioning

confidence: 99%

Threshold Analysis of Wavelet Based Fingerprint Feature Extraction Methods on Multiple Impression Dataset

Amoako-Yirenkyi

Frempong²,

Appati³

et al. 2015

BJMCS

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In recent years, fingerprint recognition has been moving through series of evolutions with the intent to decrease the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) in order to achieve minimum Equal Error Rate (EER) while increasing recognition rate. In practical cases, fingerprint images stored in fingerprint databases may have come from scanners with different specifications under variant environmental conditions which may produce different or multiple impressions and backgrounds. The choice of what single and acceptable threshold value to use in order to characterize fingerprint features in images for recognition is therefore crucial in establishing a minimal EER. In this paper, we investigate and analyze the effect of several threshold values on EER when several families of wavelets based methods for feature extraction are applied on multiple impression datasets (Fingerprint Verification Competition-FVC2004). After conducting several threshold analysis on extracted features from multiple impression dataset, the results show that among the closely related wavelets families studied, the Reversed Bi-Orthogonal type 3.1 wavelet, analysized with threshold value of 27 significantly topped with EER of 4.2% and a recognition rate of 95%. It however performed quite poorly outside of the threshold value incating the importance of threshold analysis on datasets used for recognition.

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A Fingerprint Verification Algorithm Using the Smallest Minimum Sum of Closest Euclidean Distance

Cited by 12 publications

References 7 publications

Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence Program

Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence Program

Harnessing fluorescent carbon quantum dots from natural resource for advancing sweat latent fingerprint recognition with machine learning algorithms for enhanced human identification

Threshold Analysis of Wavelet Based Fingerprint Feature Extraction Methods on Multiple Impression Dataset

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