“A fingerprint spoofing detection system using LBP”

Kulkarni, Samruddhi Shreeram; Patil, Hemprasad Yashwant

doi:10.1109/iceeot.2016.7755337

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…A novel feature extraction method for detecting fingerprint liveness according to the localized phase quantization has been introduced by Ghiani and his colleagues [20]. In addition, some studies have used other features such as skin deformation and fingerprint pores to detect liveness [21,22]. For example, Espinoza and his colleagues suggested an approach through comparing pore numbers between real live and fake fingerprints [23].…”

Section: Related Workmentioning

confidence: 99%

A Novel Fingerprint Liveness Detection Method using Empirical Mode Decomposition and Neural Network

Tong,

2023

IJACSA

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One of the most common biometric systems is fingerprint identification, which has been misused due to issues such as fraud. Hence, intelligent methods should be designed and used to recognize real-live fingerprints. Therefore, in the current work, we proposed a novel liveness fingerprint detection framework with low computational cost and excellent accuracy based on empirical mode decomposition and neural network to distinguish real from fake fingerprints. Our proposed scheme works based on empirical mode decomposition technique. The fingerprint images were cropped into 200 × 200 images and then the two-dimensional (2D) images were converted into onedimensional (1D) data, greatly reducing the computational process. The empirical mode decomposition (EMD) technique decomposed the data and the first five intrinsic mode functions (IMFs) were targeted for feature extraction through simple statistical features. The findings revealed that our suggested system can yield an average accuracy of 97.72% in distinguishing fake from real fingerprints through multilayer perceptron (MLP) neural network. This framework is very efficient compared to other techniques because only one piece of fingerprint image is enough to defend against spoof attacks. Therefore, such framework can reduce the cost of the fingerprint biometric systems, as no further hardware is needed. In addition, our framework method gives the best classification results in comparison to other previous techniques in real-live fingerprint recognition while being simple with lower computational cost. Therefore, this framework can be practically used in commercial biometric systems.

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Section: Related Workmentioning

confidence: 99%

A Novel Fingerprint Liveness Detection Method using Empirical Mode Decomposition and Neural Network

Tong,

2023

IJACSA

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“…The authors in [4], [42] proposed to study these differences in the frequency domain. Some authors proposed to analyze in spatial domain by using a local texture descriptors, such as Local Binary Pattern (LBP) [43]- [45], Local Graph Structure (LGS) [46], [47]. Different from the other authors who study only the gray image, [7] proposed to exploit the texture-information in color spaces.…”

Section: ) Static Approachesmentioning

confidence: 99%

Face Presentation Attack Detection Based on a Statistical Model of Image Noise

et al. 2019

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The vulnerability of most existing face recognition and authentication systems against face presentation attacks (a.k.a. face spoofing attacks) has been mentioned and studied in many works. This paper introduces a novel parametric approach for face PAD using a statistical model of image noise. In fact, facial images from a presentation attack contain specific textural information caused by the presentation process which makes them different from bona-fide images. The subtle difference between bona-fide and presentation attack images can be interpreted by the difference regarding noise statistics within the skin zone of the face. Our solution is casted in the hypothesis testing framework. A new database for face PAD containing face bona-fide images and images of high-quality presentation attacks has been also introduced. The performance of the proposed approach was proven in the mentioned database. Experimental results show that, in a controlled situation, our solution performs better than the other approaches in the literature.

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“…Adanya penelitian mengenai deteksi sidik jari palsu dapat membantu menekan kasus pemalsuan sidik jari yang dapat merugikan berbagai pihak, dengan menambahkan fitur untuk memeriksa citra sidik jari yang diinputkan termasuk sidik jari asli atau sidik jari tiruan [6]. Berdasarkan masalah tersebut, peneiti melakukan penelitian untuk mendeteksi suatu citra sidik jari termasuk sidik jari asli atau sidik jari tiruan.…”

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Analisis Implementasi Contrast Limited Adaptive Histogram Equalization (Clahe) Untuk Deteksi Citra Sidik Jari Tiruan

Nuraisha

Handayani²

2021

Com, Engine, Sys, Sci

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Autentikasi biometrik dengan sidik jari paling sering digunakan untuk sistem keamanan atau autentikasi sebuah akun. Seiring dengan berkembangnya model sistem keamanan menggunakan autentikasi sidik jari, muncul masalah baru yaitu penggunaan sidik jari Penggunaan sidik jari palsu dapat dilakukan melalui scanner sidik jari yang menerima salinan dari sidik jari asli yang sering disebut dengan artificial fingerprints. Penggunaan sidik jari palsu dapat mengancam keamanan dari sebuah sistem. Permasalahan deteksi sidik jari dan identifikasi bahan yang dapat meniru karakteristik sidik jari diperburuk oleh dua hal, pertama, sensor standar tidak mampu membedakan citra dari sidik jari asli dan sidik jari replika. Kedua, seringkali tidak ada isyarat yang jelas bahwa citra tersebut berasal dari sidik jari replika atau dengan kata lain sidik jari replika yang sangat mirip dengan sidik jari asli sehingga sulit untuk dibedakan. Penelitian ini bertujuan untuk mendeteksi citra sidik jari tiruan dengan tingkat akurasi yang tinggi. Dataset yang digunakan merupakan dataset publik ATVS. Metode yang diusulkan yaitu ekstraksi fitur citra sidik jari dengan kontras GLCM (Gray Level Co-Occurance Matrix) dengan metode peningkatan kualitas citra CLAHE (Contrast Limited Adaptive Histogram Equalization). Hasil deteksi citra sidik jari menggunakan CLAHE menghasilkan akurasi yang lebih baik dibandingkan tanpa menggunakan CLAHE

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“A fingerprint spoofing detection system using LBP”

Cited by 12 publications

References 14 publications

A Novel Fingerprint Liveness Detection Method using Empirical Mode Decomposition and Neural Network

A Novel Fingerprint Liveness Detection Method using Empirical Mode Decomposition and Neural Network

Face Presentation Attack Detection Based on a Statistical Model of Image Noise

Analisis Implementasi Contrast Limited Adaptive Histogram Equalization (Clahe) Untuk Deteksi Citra Sidik Jari Tiruan

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