Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning
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Abstract (arXiv)
Facial recognition systems have become an integral part of the modern world. These methods accomplish the task of human identification in an automatic, fast, and non-interfering way. Past research has uncovered high vulnerability to simple imitation attacks that could lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. Attack detection systems use a variety of facial features and advanced machine learning models for uncovering the presence of attacks. In this work, we assess existing work on liveness detection and propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 27 Aug 2024.
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10 of 37 references.
References · 10
- 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
- 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
- 2015face anti-spoofing based on color texture analysiscited by 82
- 2019Deep Pixel-wise Binary Supervision for Face Presentation Attack Detectioncited by 76
- 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
- 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 64
- 2021Self-Domain Adaptation for Face Anti-Spoofingcited by 49
- 2020DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofingcited by 25
- 2020On Improving Temporal Consistency for Online Face Liveness Detectioncited by 10
- 2019Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecturecited by 7
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