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State-of-the-art live face verification methods would be easily attacked by recorded facial expression sequence. This work directly addresses this issue via proposing a patch-wise motion parameterization based verification network infrastructure. This method directly explores the underlying subtle motion difference between the facial movements re-captured from a planer screen (e.g., a pad) and those from a real face; therefore, interactive facial expression is no longer required. Furthermore, inspired by the fact that, we embed our network into a multiple instance learning framework, which further enhance the recall rate of the proposed technique. Extensive experimental results on several face benchmarks well demonstrate the superior performance of our method.

Live Face Verification with Multiple Instantialized Local Homographic Parameterization