Effect of Face Tampering on Face Recognition

Singh, Amarjot; Singh, Sanjay Kumar

doi:10.5121/sipij.2013.4407

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…In [ 13 ], the authors extracted essential information for discrimination using a Lambertian model. Singh et al [ 14 ] proposed a method to classify real faces based on a second-order gradient. This approach focuses on differences between skin surfaces of real and fake faces.…”

Section: Related Workmentioning

confidence: 99%

Face Liveness Detection Using Defocus

Kim

Ban

Lee

2015

Sensors

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In order to develop security systems for identity authentication, face recognition (FR) technology has been applied. One of the main problems of applying FR technology is that the systems are especially vulnerable to attacks with spoofing faces (e.g., 2D pictures). To defend from these attacks and to enhance the reliability of FR systems, many anti-spoofing approaches have been recently developed. In this paper, we propose a method for face liveness detection using the effect of defocus. From two images sequentially taken at different focuses, three features, focus, power histogram and gradient location and orientation histogram (GLOH), are extracted. Afterwards, we detect forged faces through the feature-level fusion approach. For reliable performance verification, we develop two databases with a handheld digital camera and a webcam. The proposed method achieves a 3.29% half total error rate (HTER) at a given depth of field (DoF) and can be extended to camera-equipped devices, like smartphones.

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Section: Related Workmentioning

confidence: 99%

Face Liveness Detection Using Defocus

Kim

Ban

Lee

2015

Sensors

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“…Pengenalan wajah merupakan proses untuk mengidentifikasi seseorang berdasarkan karakteristik wajah yang dimiliki. Sistem ini banyak diterapkan pada berbagai kebutuhan, seperti: absensi, keamanan ruangan dan interaksi manusia dengan komputer (Singh & Singh, 2013).…”

Section: Pengenalan Wajahunclassified

Kombinasi Jaringan Learning Vector Quantization Dan Normalized Cross Correlation Pada Pengenalan Wajah

Saleh¹,

Indra²,

Harahap³

2020

JUSIKOM

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Pengenalan wajah merupakan cara yang dilakukan untuk mengidentifikasi wajah berdasarkan nilai ciri yang terdapat pada citra wajah dan dapat diterapkan di dalam berbagai sistem, seperti absensi, akses keamanan ruangan dan login aplikasi atau perangkat. Salah satu algoritma untuk pengenalan wajah adalah LVQ (learning vector quantization), tetapi dalam pemilihan bobot awal yang kurang tepat dapat berdampak pada penurunan kinerja algoritma tersebut, sehingga hasil dari pengenalan wajah kurang akurat. Permasalahan ini dapat diselesaikan dengan penentuan bobot awal yang tepat dengan metode tertentu. Bobot yang dipilih pada penelitian ini berdasarkan kemiripan citra, salah satu metode untuk mengukur kemiripan adalah NCC (Normalized Cross Correlation). Penelitian ini akan dilakukan dengan mengkombinasi jaringan LVQ dengan menggunakan NCC dalam penentuan bobot awal untuk pengenalan wajah. Hasil pengujian yang diperoleh dengan kombinasi kedua metode tersebut untuk pengenalan wajah sebesar 94%.

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“…Aruni Singh and Sanjay Kumar Singh [14] addressed face tampering effect on four face recognition algorithm categories. Accuracy of each algorithm is varying and unpredictable.…”

Section: Introductionmentioning

confidence: 99%

Synergy of Schur, Hessenberg and QR Decompositions on Face Recognition

Jagadeesh¹,

K²,

Raja³

2016

IJIGSP

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Human recognition through faces has elusive challenges over a period of time. In this paper, an efficient method using three matrix decompositions for face recognition is proposed. The proposed model uses Discrete Wavelet Transform (DWT) with Extended Directional Binary codes (EDBC) in one branch. Three matrix decompositions combination with Singular Value Decomposition (SVD) is used in the other branch. Preprocessing uses Single Scale Retinex (SSR), Multi Scale Retinex (MSR) and Single scale Self Quotient (SSQ) methods. The Approximate (LL) band of DWT is used to extract one hundred EDBC features. In addition, Schur, Hessenberg and QR matrix decompositions are applied individually on pre-processed images and added. Singular Value Decomposition (SVD) is applied on the decomposition sum to yield another one hundred features. The combination EDBC and SVD features are final features. City-block or Euclidean Distance (ED) measures are used to generate the results. Performance on YALE, GTAV and ORL face datasets is better compared to other existing methods.

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Effect of Face Tampering on Face Recognition

Abstract: ABSTRACT

Cited by 4 publications

References 39 publications

Face Liveness Detection Using Defocus

Face Liveness Detection Using Defocus

Kombinasi Jaringan Learning Vector Quantization Dan Normalized Cross Correlation Pada Pengenalan Wajah

Synergy of Schur, Hessenberg and QR Decompositions on Face Recognition

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