Deep learning

Sociology

Pattern recognition (psychology)

Mathematics

Social science

Binary classification

Margin (machine learning)

Discriminative model

Support vector machine

Machine learning

GRASP

Artificial intelligence

Spoofing attack

Programming language

Face (sociological concept)

Facial recognition system

Computer science

Computer vision

Computer security

Binary number

Arithmetic

Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues. A CNN-RNN model is learned to estimate the face depth with pixel-wise supervision, and to estimate rPPG signals with sequence-wise supervision. The estimated depth and rPPG are fused to distinguish live vs. spoof faces. Further, we introduce a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations. Experiments show that our model achieves the state-of-the-art results on both intra- and cross-database testing.

Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision