Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario.
Code (3)
Tasks
Ensemble LearningFace Anti-Spoofingmotion predictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Advancing Cross-Domain Generalizability in Face Anti-Spoofing: Insights, Design, and Metrics
This paper presents a novel perspective for enhancing anti-spoofing performance in zero-shot data domain generalization. Unlike traditional image classification tasks, face anti-spoofing datasets display unique generaliz…
Domain GeneralizationFace Anti-Spoofingimage-classificationImage ClassificationAdaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors…
Face Anti-SpoofingDeep Transfer Across Domains for Face Anti-spoofing
A practical face recognition system demands not only high recognition performance, but also the capability of detecting spoofing attacks. While emerging approaches of face anti-spoofing have been proposed in recent years…
Face Anti-SpoofingFace RecognitionA Multi-Modal Approach for Face Anti-Spoofing in Non-Calibrated Systems using Disparity Maps
Face recognition technologies are increasingly used in various applications, yet they are vulnerable to face spoofing attacks. These spoofing attacks often involve unique 3D structures, such as printed papers or mobile d…
Face Anti-SpoofingFace RecognitionFeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Face Anti-spoofing gains increased attentions recently in both academic and industrial fields. With the emergence of various CNN based solutions, the multi-modal(RGB, depth and IR) methods based CNN showed better perform…
DiversityFace Anti-Spoofing