Style selective normalization with meta learning for test-time adaptive face anti-spoofing

Kim, Young-Eun; Nam, Woo-Jeoung; Min, Kyungseo; Lee, Seong-Whan

doi:10.1016/j.eswa.2022.119106

Cited by 4 publications

(2 citation statements)

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“…Majority of the existing methods use domain adaptation to minimize domain variation. Recently, Kim et al [ 164 ] proposed a new face PAD method that uses Meta style selective normalization with domain adaptation which detects domain-centric styles of specific domains. The parameters are selected with optimal normalization by reducing the discrepancies between source and target domains.…”

Section: State-of-the Art Face Pad Mechanismsmentioning

confidence: 99%

“…Al. [ 163 ] TSViT framework for PAD Print, display, and video O-N, C-M and R-A ~ 0.0% HTER C-M R-A ACER = 23.6% DT12 2022 Abdullakutty et al [ 165 ] Deep transfer learning Print, display, and video NUAA, C-F, SiW and R-A Results outperform the state-of-the-art approaches Aggregated data SiW ACC = 62.87% DT13 2023 Kim Y. M. et al [ 164 ] Meta Style Selective Normalization (MetaSSN) + domain adaptation Print, display, and video C-F, M-M, O-N, I-RA – C-F, O-N, I-RA M-M 10.8 M-M, O-N, I-RA C-F 20.5 C-F, M-M, O-N, I-RA 11.3 C-F, M-M, I-RA O-N 16.4 RA = Replay-Attack, CF = CASIA-FASD, MM = MSU-MFD, ON = OULU-NPU, 3DMAD = 3 Dimensional attack database, R-Y = ROSE Youtu, NI = NUAA Imposter, P-A = Print Attack, USSA = Unconstrained Smartphone Spoof Attack database, IRA = Idiap Replay-Attack …”

Section: State-of-the Art Face Pad Mechanismsmentioning

confidence: 99%

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A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Sharma

Selwal

2023

Multimedia Systems

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The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions.

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Section: State-of-the Art Face Pad Mechanismsmentioning

confidence: 99%