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Face spoofing detection using Multi-Level Local Phase Quantization (ML-LPQ)

Abstract : Biometric technologies are becoming the foundationof an extensive array of highly secure identification and verifica-tion solution. Unfortunately, biometric systems are vulnerable toattacks made by persons showings photo, video or mask to spoofthe real identity. In this paper we study a solution for those prob-lems. We try to make solution to face spoofing for distinguishingbetween real face and fake one. Our approach called Multi-LevelLocal Phase Quantization (ML-LPQ) is focused in Local PhaseQuantization (LPQ) descriptor for extracting features on faceregion of interest. In our approach, we use three levels for theLPQ descriptor to extract features and LibSVM for classification.Our experimental analysis on a publicly available CASIA faceanti-spoofing database give us good result compared to otherapproaches using the same protocol
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Conference papers
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https://hal-uphf.archives-ouvertes.fr/hal-03412422
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Wednesday, November 3, 2021 - 9:08:53 AM
Last modification on : Thursday, November 4, 2021 - 4:07:51 AM

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  • HAL Id : hal-03412422, version 1

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Azeddine Benlamoudi, Djamel Samai, Abdelkrim Ouafi, Salah Eddine Bekhouche, Abdelmalik Taleb-Ahmed, et al.. Face spoofing detection using Multi-Level Local Phase Quantization (ML-LPQ). International Conference on Automatic control, Telecommunications and Signals (ICATS15), Nov 2015, Annaba, Algeria. ⟨hal-03412422⟩

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