Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
学习人脸反欺骗的深度模型：二进制或辅助监控 摘要 人脸反欺骗是防止人脸识别系统的安全漏洞的关键。以往的深度学习方法将人脸反欺骗表述为一个二值分类问题。他们中的许多人很难掌握足够的欺骗线索，因而无法进行有效的归纳。在本文中，我们认为辅助监督对于引导朝着区分性和普遍性限线索的学习的很重要。利用CNN-RNN模型对人脸深度进行像素监督估计对rPPG信号进行序列监督评估。估计深度和rPPG信号被融合来区分
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相关文章
- 1. 单目可见光静默活体检测 Binary or Auxiliary Supervision论文解读
- 2. 《Learning Deep Representation for Face Alignment with Auxiliary Attributes》论文阅读笔记
- 3. A Discriminative Feature Learning Approach for Deep Face Recognition
- 4. Deep Learning Models and Code for Pose Estimation
- 5. Deep Learning Models on Kubernetes with GPUs
- 6. 论文：Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
- 7. Deploying deep learning models in OpenCV
- 8. Learning Invariant Deep Representation for NIR-VIS Face Recognition
- 9. awesome-face-anti-spoofing
- 10. Deep Learning for NLP Best Practices