Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM

Hsieh, Ching-Tang; Hu, Chia-Shing

doi:10.1016/s1665-6423(14)71662-1

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…But when the quality of fingerprinted image is poor is extremely unreliable. There many work that lets Minutia extraction by using different classifier as SVM [36], Bayes [37], Neuron, Fuzzy, etc. Table 1 resumes the most important contributions.…”

Section: A Discussionmentioning

confidence: 99%

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An Accelerated Architecture Based on GPU and Multi-Processor Design for Fingerprint Recognition

Ayed¹,

Elkosantini²

2016

ijacsa

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Abstract-Fingerprint recognition is widely used in security systems to recognize humans. In both industry and scientific literature, many fingerprint identification systems were developed using different techniques and approaches. Although the number of conducted research works in this field, developed systems suffer for some limitations partially those related the real time computation and fingerprint recognition. Accordingly, this paper proposes a reliable algorithm for fingerprint recognition based on the extraction and matching of Minutiae. In this paper, we present also an accelerated architecture based on GPU and multi-processor design in which the suggested fingerprint recognition algorithm is implemented.

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Section: A Discussionmentioning

confidence: 99%

“…[32] 4.18% 9.93% OMER SAEED method [33] 1,12% Not indicated Ying HAO method [34] 1% 2.5% Jiong Zang method [35] 0.04% 1.31% Ching-Tang and al. method [36] 0.5% 0% Mossaad and al. method [27] 0% 0.02%…”

Section: A Discussionmentioning

confidence: 99%

An Accelerated Architecture Based on GPU and Multi-Processor Design for Fingerprint Recognition

Ayed¹,

Elkosantini²

2016

ijacsa

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“…Arsitektur ini dapat memanfaatkan informasi struktural pada data yang membantu dalam membedakan kelas-kelas tertentu. Metode SVM juga memberikan kinerja tinggi seperti pada riset oleh Ching Tang dan Chia Shing (2014) menghasilkan error rate sebesar 0.5% [7]. Pada penelitian ini terdapat citra sidik jari yang memiliki posisi pengambilan tidak stabil akibat human error.…”

Section: Abstrakunclassified

“…Filter Gabor bekerja sebagai filter bandpass untuk distribusi frekuensi spasial lokal, mencapai resolusi optimal dalam domain baik spasial dan frekuensi [7]. Berikut persamaan dari filter Gabor:…”

Section: Studi Literatur 1) Filter Gaborunclassified

Analisis dan Implementasi Metode Gabor Filter dan Support Vector Machine pada Klasifikasi Sidik Jari

Wijaya

2017

IndoJC

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Identity is the most important thing for identification process. Identification processes that are increasingly vulnerable to hacking require secure system input. Biometrics is a solution for input identification systems that are not vulnerable to hacking. one type of biometric input is a fingerprint because it is unique to each individual and does not change over time. Fingerprint identification system can be done by classification. The classification in this study uses the Gabor filter method with four orientation angles that range from 0 0 , 45 0 , 90 0 and 135 0 as fingerprint feature extraction, and Support Vector Machine (SVM) one against all as classifier. With 25 classes and 3 data per fingerprint class obtained the greatest accuracy by radial Basis Function (RBF) kernel for 73% for initial data and 76% for additions augmentation data. The difference in accuracy is due to the possibility of more data position changes in the augmented fingerprint image.Keywords: Biometrics, Fingerprint, Support Vector Machine, Gabor Filter Abstrak Identitas merupakan hal utama pada proses identifikasi. Proses identifikasi yang semakin rentan dengan peretasan membutuhkan masukan sistem yang aman. biometrik adalah solusi untuk masukan sistem identifikasi yang tidak rentan peretasan. Salah satu jenis masukan biometri adalah sidik jari karena sifatnya yang unik setiap individu dan tidak berubah seiring waktu. Sistem identifikasi dengan sidik jari dapat dilakukan dengan klasifikasi. Klasifikasi pada penelitian ini menggunakan metode Gabor filter dengan empat sudut orientasi yang berkisar 0 0 , 45 0 , 90 0 dan 135 0 sebagai ekstraksi ciri sidik jari, serta Support Vector Machine (SVM) one against all sebagai classifier. Dengan 25 kelas dan 3 data tiap kelas sidik jari diperoleh akurasi terbesar oleh kernel Radial Basis Function (RBF) sebesar 73% untuk data awal dan 76% untuk penambahan data augmentasi. Perbedaan akurasi disebabkan oleh kemungkinan perubahan posisi data yang lebih banyak pada citra sidik jari yang diaugmentasi.Kata Kunci: Sistem Biometri, Sidik Jari, Support Vector Machine, Gabor Filter Received on xxx, accepted on xxx, published on xxx I. PENDAHULUAN I DENTITAS merupakan unsur penting bagi setiap orang yang sering dipakai dalam kebutuhan seharihari. Proses pengenalan identifikasi secara umum identifikasi terbagi menjadi dua cara yakni berdasarkan benda kepemilikan seperti kartu identitas (ATM, KTP, SIM, dan lainnya) dan berdasarkan bagian OPEN ACCESS

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“…In this study, for pipeline leakage accident identification and leakage point localization, an SVM‐based model is combined with the particle swarm optimization (PSO) algorithm to find the best parameter combination. PSO, as an evolutionary computation technique, is a population‐based optimization tool inspired by the social behavior among individuals. Recently, several studies on PSO theories and applications have been reported.…”

Section: Introductionmentioning

confidence: 99%

Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

Jia

Ren

et al. 2018

Struct Control Health Monit

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Summary A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.

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Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM

Cited by 11 publications

References 12 publications

An Accelerated Architecture Based on GPU and Multi-Processor Design for Fingerprint Recognition

An Accelerated Architecture Based on GPU and Multi-Processor Design for Fingerprint Recognition

Analisis dan Implementasi Metode Gabor Filter dan Support Vector Machine pada Klasifikasi Sidik Jari

Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

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