An Embedded Fingerprint Authentication System

Shinde, Archana S.; Bendre, Varsha

doi:10.1109/iccubea.2015.45

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…The fingerprint identification system primarily encompasses fingerprint acquisition, fingerprint enhancement, feature extraction, and fingerprint matching among other aspects [45]. Fingerprint image acquisition serves as the initial step in the fingerprint recognition system, while fingerprint enhancement involves repairing the quality of the captured fingerprint image.…”

Section: B Fingerprint Recognitionmentioning

confidence: 99%

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Liu,

Zhi,

Zhao

et al. 2024

IEEE Access

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Fingerprint recognition technology has been extensively employed across various sectors of society. The direct publication of fingerprint images leads to the disclosure of sensitive information. According to the fingerprint image identification process, the fingerprint image protection process is actually the fingerprint image feature point location information, quantity information, type information protection. To address this issue, this paper proposes a machine learning and differential privacy-based fingerprint image publish algorithm called DP-RKLAP. The algorithm establishes a protected process to match feature points in fingerprint images and employs a clustering algorithm for initial segmentation of the images (KLAP). Additionally, a multinomial regression algorithm is applied to preprocess the segmented image regions, constructing a regression model that accurately determines fluctuation amplitudes for precise segmentation of protected areas containing matching feature points (RKLAP). Considering the uncertainty in segmentation caused by uncertain feature point locations in fingerprint images, we introduce a dynamic allocation method (DP) for privacy budget allocation. The exponential mechanism leverages the relationship between the number of matching feature points and segmentation regions to dynamically allocate privacy budgets within the Laplace mechanism framework of differential privacy technique, thereby achieving local protection publish for fingerprint images. This reduction in sensitivity effectively mitigates noise errors during the process of privacy protection, thereby achieving a balance between privacy and usability of the fingerprint images. Experimental results confirm that our proposed method successfully achieves privacy protection during the publishing process of fingerprint images, while still maintaining high usability after protected publishing and matching verification using real-world datasets.

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Section: B Fingerprint Recognitionmentioning

confidence: 99%

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Liu,

Zhi,

Zhao

et al. 2024

IEEE Access

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“…Karena itu, sidik jari sering dijadikan sebagai identitas (Tjandra & Kistijantoro, 2018). Autentikasi sidik jari adalah salah satu metode identifikasi individu yang paling dapat diandalkan dan banyak digunakan (Shinde & Bendre, 2015) hal ini dikarenakan autentikasi sidik jari dapat melindungi keamanan sistem dan meningkatkan efisiensi sistem (Xu & Deng, 2016).…”

Section: Pendahuluanunclassified

Perbandingan Ekstraksi Fitur dan Proses Matching pada Autentikasi Sidik Jari Manusia

et al. 2020

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ABSTRAK Sidik jari merupakan biometrik yang sering digunakan dalam teknologi autentikasi. Terdapat banyak metode yang bisa digunakan untuk membuat sistem klasifikasi sidik jari. Maximum Curvature Points (MCP) umumnya digunakan untuk ekstraksi citra pembuluh darah jari yang juga digunakan sebagai autentikasi. Pada penelitian ini akan diuji performansi dari metode MCP jika dibandingkan dengan metode yang umum digunakan pada proses pengenalan sidik jari, yaitu Hit and Miss Transform (HMT). Perbedaan domain, yaitu spasial pada Normalized Cross Correlation (NCC) dan frekuensi pada Phase Correlation (PC) dalam proses matching ternyata juga mempengaruhi performansi sistem. Hasilnya menunjukkan bahwa penggunakaan metode MHTNCC memiliki tingkat akurasi yang lebih baik dalam pengenalan sidik jari yaitu 92% untuk pengenalan ibu jari dan 98% untuk pengenalan jari telunjuk, dibandingkan dengan menggunakan metode MCP-PC yang hanya memiliki tingkat akurasi sebesar 88% untuk pengenalan ibu jari dan 92% untuk pengenalan jari telunjuk. Kata kunci: sidik jari, MCP, HMT, phase correlation, normalized cross correlation ABSTRACT Fingerprint is one of the biometric systems that are often used in an authentication technology. There are many methods that can be used to develop fingerprint’s classification system. Maximum Curvature Points (MCP) are generally used for finger vein image extraction which is also used as authentication. MCP performance will be compared to common method in finger print recognition, Hit and Miss Transform (HMT). Using different domains, spatial in Normalized Cross Correlation (NCC) and frequency in Phase Correlation (PC) affect the system performance. The results show that the application of HMT-NCC more accurate in terms of finger print’s recognition, 92% in accuracy for thumb recognition and 98% accuracy for index finger recognition, while MCP-PC is only reach 88% in accuracy for thumb recognition and 92% accuracy for index finger recognition. Keywords: fingerprint, MCP, HMT, phase correlation, normalized cross correlation

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“…The extraction stage comprises extracting minutiae points in a fingerprint. This includes the removal of an important detail of fingerprints, and then sends it to store in the database [22]. The task of the matching stage, to compare and check pattern prints fingers the modes of operation of the system are mainly in two ways [23,24]: a) Enrolment/Registration mode: at this stage, called enrolment, the system measures the biometric characteristics of the user.…”

Section: Introductionmentioning

confidence: 99%

Real time model of a lock gate system upon fingerprint with microcontroller

Mahammad

Mashhadany

Eyada

2021

IJEECS

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<span>Safety is one of the most important things that we are keen to provide all aspects of our lives. Keyless lock gate with fingerprint access has many advantages over traditional systems such as passwords. Therefore, fingerprint identification represents the main part, which require that system is cheap, practical, easy to use and allows fast and securely. This paper discusses the design and implementation with real time model of a practical lock gate system for the achievement of high security by using fingerprint purification to control the mechanism of opening doors in banks, warehouses, laboratories and places of interest. In this paper, the microcontroller technology in the Ardonio board was used with an optical sensor to take the fingerprint, convert it into digital data, and insert it into a definition window to unify all input data and store it for use when compared to the system experience. After obtaining the result of the comparison, the appropriate commands are prepared for operating the servo of the lock mechanism for the doors used. In the testing of the system, the results are very accurate. The system also has other advantages such as small size and low power required for operation and ease of use. </span>

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An Embedded Fingerprint Authentication System

Cited by 13 publications

References 8 publications

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

Perbandingan Ekstraksi Fitur dan Proses Matching pada Autentikasi Sidik Jari Manusia

Real time model of a lock gate system upon fingerprint with microcontroller

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