Access Control System using fingerprint recognition

Mahadik, Swapnali; Narayanan, K. Lakshmi; Bhoir, Deepak; Shah, Darshana T.

doi:10.1145/1523103.1523166

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…With the new rotation-invariant reference point location method, L. Sha, F. Zhao, and X. Tang [18] merged the orientation elements and AAD characteristics to develop an oriented Finger Code. The image was rotated cyclically to create 10 patterns to mimic its angle constancy properties.…”

Section: ) Feature Extraction Using Gabor Filter and Matchingmentioning

confidence: 99%

Survey on Fingerprint Identification

2023

IRJMETS

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In the paper, reviewed a few of the more recent studies on the recognition of fingerprints method. Identification using fingerprints is a method for identifying humans by their distinct pattern of lines and furrows on their fingertips. Because each person has an individual set of ridges or furrows on their finger, fingerprints are a reliable method of identifying. Fingerprint verification is often employed in forensic science, law enforcement agencies, and safety precautions for control of entry and recognition. The fingertips of somebody can frequently be checked using a gadget fingerprint scanner or detectors as part of the identification procedure. Using a camera or sensor, valleys and hills of the outermost portion are considered, and this image is then analyzed by software for displaying features and traits.

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Section: ) Feature Extraction Using Gabor Filter and Matchingmentioning

confidence: 99%

Survey on Fingerprint Identification

2023

IRJMETS

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“…When the image has distortions, it is important to find the right algorithm that can reduce or even remove inaccuracies. Fingerprint contact ridge details are normally styled in a classified order at three levels, namely, First Level (pattern), Second Level (minutiae points), and Third Level (pores and ridge contours) [5,14]. Traditionally, they distinguish general and individual features.…”

Section: Fingerprint Recognitionmentioning

confidence: 99%

Survey of Primary Methods of Fingerprint Feature Extraction

Wahhab¹,

Alanssari²

2018

CTAC&R

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Proving the identity of the individual today is an urgent need in many areas, including social security, financial, criminal and other fields. Biometrics is interested in identifying individuals through personality traits, eye color, fingerprints, height, facial appearance, and signature. Image processing technology supports most of the techniques used in biometrics, by improving images and matching images, to identify the individual identity. Fingerprint recognition is some of the most well-known biometrics, and it the best the used biometric result for confirmation on computer systems. Technic fingerprinting has a great popularity for simplicity of acquisition, the use and approval when compared to other systems. Fingerprint Recognition System consists of four steps: firstly, Image acquisition (image fingerprint) through sensors .Secondly, processing the image so that it obscures the noise that exists, clarifying the hills. Thirdly, Extracting features of injury fingerprints. Fourth, compare the acquired features with features of fingerprint fingerprints in databases. The aim of this paper is to present the latest research in the applications of fingerprint recognition systems.

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“…The mutual information at each step of Rotation and translation is calculated to find the best alignment of fingerprint by maximizing the mutual information. Swapnali Mahadik et al, [20] described an Alignment based Minutiae Matching algorithm. The minutiae extraction involves Filtering, Binarization, Orientation Estimation, Region of interest, Thinning and Minutiae Extraction.…”

Section: Related Workmentioning

confidence: 99%

Fingerprint Verification based on fusion of Minutiae and Ridges using Strength Factors

R.¹,

Chhotaray²,

Raja³

et al. 2010

IJCA

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With the increasing emphasis on the automatic personal identification applications, biometrics especially fingerprint identification is the most reliable and widely accepted technique. In this paper Fingerprint Verification based on fusion of Minutiae and Ridges using Strength Factors (FVMRSF) is presented. In the preprocessing stage the Fingerprint is Binarised and Thinned. The Minutiae Matching Score is determined using Block Filter and Ridge matching score is estimated using Hough Transform.The strength factors Alpha (α) and Beta (β) are used to generate Hybrid matching score for matching of fingerprints. The proposed algorithm has better matching percentage for different fingerprints compared to the existing algorithms.

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Access Control System using fingerprint recognition

Cited by 8 publications

References 7 publications

Survey on Fingerprint Identification

Survey on Fingerprint Identification

Survey of Primary Methods of Fingerprint Feature Extraction

Fingerprint Verification based on fusion of Minutiae and Ridges using Strength Factors

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