A simple and effective patch-Based method for frame-level face anti-spoofing

With the wide applications of face recognition, face anti-spoofing has become a major challenge for reli-able face recognition. Thus, it is necessary to perform face liveness detection. Most existing methods rely on a whole face image for training and testing and are thus susceptible to the overfitting problem be-cause of limited training samples; meanwhile, liveness information is not fully explored. To address these issues, we propose a simple and effective patch-based approach. There are two main contributions: 1) different patch sampling strategies are applied to a training set and a testing set to overcome the over -fitting problem, and 2) an attention mechanism is applied to explore more significant information for liveness detection. We evaluate the proposed approach on four popular and challenging databases: the CASIA-SURF, OULU-NPU, CASIA-FASD and REPLAY-ATTACK databases. The proposed method could obtain very promising liveness detection performance. For example, the average classification error rate (ACER) on the CASIA-SURF database (using RGB images only) was 1.6%, which is the lowest reported error rate to the best of our knowledge.(c) 2023 Elsevier B.V. All rights reserved.