Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression
The paper addresses face presentation attack detection in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not present in the training step. For this purpose, a pure one-class face presentation attack detection approach based on kernel regression is developed which only utilises bona fide (genuine) samples for training. In the context of the proposed approach, a number of innovations, including multiple kernel fusion, client-specific modelling, sparse regularisation and probabilistic modelling of score distributions are introduced to improve the efficacy of the method. The results of experimental evaluations conducted on the OULU-NPU, Replay-Mobile, Replay-Attack and MSU-MFSD datasets illustrate that the proposed method compares very favourably with other methods operating in an unseen attack detection scenario while achieving very competitive performance to multi-class methods (benefiting from presentation attack data for training) despite using only bona fide samples for training.
Code (0)
등록된 구현이 없습니다.
Tasks
Face Presentation Attack DetectionregressionSimilar Papers 제목 키워드 기반
Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels
The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown types of attack. While earlier studies have revealed the benefits of ensemble methods, and in particular, …
Face Presentation Attack DetectionNovelty DetectionLearning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to add…
Face Anti-SpoofingFace Presentation Attack DetectionFace RecognitionOne-class classifierClient-Specific Anomaly Detection for Face Presentation Attack Detection
The one-class anomaly detection approach has previously been found to be effective in face presentation attack detection, especially in an \textit{unseen} attack scenario, where the system is exposed to novel types of at…
Anomaly DetectionDecision MakingFace Presentation Attack DetectionSRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detection
Face morphing attacks represent a significant threat to biometric systems as they allow multiple identities to be combined into a single face. While supervised morphing attack detection (MAD) methods have shown promising…
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection
Face recognition systems are widely deployed for biometric authentication. Despite this, it is well-known that, without any safeguards, face recognition systems are highly vulnerable to presentation attacks. In response …
Anomaly DetectionFace Recognition