Presentation Attack Detection Methods for Face Recognition Systems

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Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks (2020, methodology): “Often, features such as color, texture [5], [6], motion [7], and physiological cues [8], [9] and CNN”
Deep Face Recognition: A Survey (2018, background): “Presentation attack [156], adversarial attack [62][59], and template attack [134] are developing”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, methodology): “Different methods have been proposed based on action imitation, speech collaboration, and multi-modal sensors [102].”
Attack Detection (2020, neutral): “This challenge was also highlighted by Ramachandra and Busch (2017), who stated, “The adoption of hardware-based approaches may provide the necessary accuracy for detecting picture, display, and video replay attacks, but it will increase the cost and computational response time of facial…”