Hybrid minutiae and edge corners feature points for increased fingerprint recognition performance

Nachar, Rabih Al; Inaty, Elie; Bonnin, Patrick; Alayli, Yasser

doi:10.1007/s10044-018-00766-z

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Currently, there is a growing number of applications using fingerprint recognition systems, such as for accessing mobile phones, monitoring employee presence in a company and, in forensic investigations, to achieve the unequivocal identification of an individual. Technological advances in fingerprint processing enable capture, storage, and comparison methods to be more financially accessible today, allowing a significant portion of the population to use these technologies [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Recognition in Forensic Scenarios

Martins,

Silva,

Bernardino

2024

Sensors

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Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

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Section: Introductionmentioning

confidence: 99%

Fingerprint Recognition in Forensic Scenarios

Martins,

Silva,

Bernardino

2024

Sensors

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“…In addition, significant changes in the brightness level of an image can correspond to ridges or valleys, and their study is important in applications such as fingerprint technologies. In this case, optimal detectors such as the Canny algorithm produce undesired results, yielding double edges, since they were not designed to detect this type of discontinuity [16]. There are different techniques to detect edges, which, according to the domain wherein they work, can be classified as spatial, frequency, and wavelet methods [1].…”

Section: Introductionmentioning

confidence: 99%

Generalized Quantification Function of Monogenic Phase Congruency

Forero,

Jacanamejoy,

Machado

et al. 2023

Mathematics

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Edge detection is a technique in digital image processing that detects the contours of objects based on changes in brightness. Edges can be used to determine the size, orientation, and properties of the object of interest within an image. There are different techniques employed for edge detection, one of them being phase congruency, a recently developed but still relatively unknown technique due to its mathematical and computational complexity compared to more popular methods. Additionally, it requires the adjustment of a greater number of parameters than traditional techniques. Recently, a unique formulation was proposed for the mathematical description of phase congruency, leading to a better understanding of the technique. This formulation consists of three factors, including a quantification function, which, depending on its characteristics, allows for improved edge detection. However, a detailed study of the characteristics had not been conducted. Therefore, this article proposes the development of a generalized function for quantifying phase congruency, based on the family of functions that, according to a previous study, yielded the best results in edge detection.

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“…At least one feature is extracted from the recognized pixel area in the color image, and the food type corresponding to the food is automatically recognized based on the extracted feature. [5].R. Manoranjitham classifies the satellite image map by a new model using a convolutional neural network modified by a support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Maize Variety Identification Based on Support Vector Machines

2021

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Seeds are the most basic and critical means of production in agricultural production. Declining purity of seed varieties will significantly reduce crop yield and product quality. The quality of crop seed varieties, especially purity testing, can protect the interests of farmers and promote the development of rural economies. The aim of this paper is to study maize variety identification based on support vector machines. Based on the morphological structure characteristics of maize varieties, a set of characteristic parameters that can accurately reflect the morphological structure of different maize varieties is proposed for variety identification. The parameter set consists of four components: shape, colour, size and sharpness parameters. The experimental results show that the support vector machine algorithm outperforms the BP neural network.

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Hybrid minutiae and edge corners feature points for increased fingerprint recognition performance

Cited by 9 publications

References 25 publications

Fingerprint Recognition in Forensic Scenarios

Fingerprint Recognition in Forensic Scenarios

Generalized Quantification Function of Monogenic Phase Congruency

Maize Variety Identification Based on Support Vector Machines

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