Face Anti-Spoofing Scheme Using Handcraft Based and Deep Learning Methods

Year 2020, Volume: 35 Issue: 4, 1103 - 1110, 31.12.2020

Omid Sharıfı

https://doi.org/10.21605/cukurovaummfd.869181

Cited By: 1

Abstract

Malicious parties which impersonate systems by fake identities affect recognition performance of biometric systems. This study focuses on a strength anti-spoofing scheme based on decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involves consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this context, convolutional neural network (CNN) and Log-Gabor filter methods are used to learn deep representations and extract facial features of images respectively. In order to improve the robustness of proposed anti-spoofing framework, fusion of Log-Gabor and CNN methods is considered by applying decision-level-fusion technique. Finally, the performance of proposed anti- spoofing scheme is examined on public spoof databases such as Print-Attack and Replay-Attack face databases to detect fake facial images.

Keywords

Spoof attacks, Handcrafted texture extraction, Convolutional neural network, Decision level fusion

El Yapımı Tabanlı ve Derin Öğrenme Yöntemlerini Kullanan Yüz Yanıltma Önleme Şeması

Abstract

Sahte kimliklerle sistemleri taklit eden kötü niyetli kişiler, biyometrik sistemlerin tanınma performansını etkilemektedir. Bu çalışma, bireyleri gerçek ve sahte terimlerle izlemek için karar düzeyinde füzyona dayalı güçlü bir sahtekarlık önleme şemasına odaklanmaktadır. Önerilen sahte tespit şeması, gerçek ve sahte bireyleri ayırt etmek için yüz görüntülerinde hem el yapımı hem de derin öğrenme tekniklerinin dikkate alınmasını içerir. Bu bağlamda, evrişimsel sinir ağı (CNN) ve Log-Gabor filtre yöntemleri sırasıyla görüntülerin derin temsillerini öğrenmek ve görüntülerin yüz özelliklerini çıkarmak için kullanılmaktadır. Önerilen sahteciliği önleme çerçevesinin sağlamlığını geliştirmek için, Log-Gabor ve CNN yöntemlerinin füzyonu, karar seviyesinde füzyon tekniği uygulanarak değerlendirilmiştir. Son olarak, önerilen sahteciliği önleme planının performansı, sahte yüz görüntülerini tespit etmek için Print- Attack ve Replay-Attack gibi halka açık veri tabanlarında incelenmiştir.

Keywords

Sahte saldırılar, El yapımı doku çıkarma, Evrişimsel sinir ağı, Karar düzeyinde füzyon

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