A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection
The vulnerability against presentation attacks is a crucial problem undermining the wide-deployment of face recognition systems. Though presentation attack detection (PAD) systems try to address this problem, the lack of generalization and robustness continues to be a major concern. Several works have shown that using multi-channel PAD systems could alleviate this vulnerability and result in more robust systems. However, there is a wide selection of channels available for a PAD system such as RGB, Near Infrared, Shortwave Infrared, Depth, and Thermal sensors. Having a lot of sensors increases the cost of the system, and therefore an understanding of the performance of different sensors against a wide variety of attacks is necessary while selecting the modalities. In this work, we perform a comprehensive study to understand the effectiveness of various imaging modalities for PAD. The studies are performed on a multi-channel PAD dataset, collected with 14 different sensing modalities considering a wide range of 2D, 3D, and partial attacks. We used the multi-channel convolutional network-based architecture, which uses pixel-wise binary supervision. The model has been evaluated with different combinations of channels, and different image qualities on a variety of challenging known and unknown attack protocols. The results reveal interesting trends and can act as pointers for sensor selection for safety-critical presentation attack detection systems. The source codes and protocols to reproduce the results are made available publicly making it possible to extend this work to other architectures.
Code (0)
등록된 구현이 없습니다.
Tasks
Face Presentation Attack DetectionFace RecognitionSimilar Papers 제목 키워드 기반
Dual Channel Multi-Attention in ViT for Biometric Authentication using Forehead Subcutaneous Vein Pattern and Periocular Pattern
Traditional biometric systems, like face and fingerprint recognition, have encountered significant setbacks due to wearing face masks and hygiene concerns. To meet the challenges of the partially covered face due to face…
FaceQSORT: a Multi-Face Tracking Method based on Biometric and Appearance Features
Tracking multiple faces is a difficult problem, as there may be partially occluded or lateral faces. In multiple face tracking, association is typically based on (biometric) face features. However, the models used to ext…
Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network
Face recognition is a mainstream biometric authentication method. However, vulnerability to presentation attacks (a.k.a spoofing) limits its usability in unsupervised applications. Even though there are many methods avai…
Face Presentation Attack DetectionFace RecognitionBenchmarking Foundation Models for Zero-Shot Biometric Tasks
The advent of foundation models, particularly Vision-Language Models (VLMs) and Multi-modal Large Language Models (MLLMs), has redefined the frontiers of artificial intelligence, enabling remarkable generalization across…
AttributeBenchmarkingDeepFake DetectionFace Swapping+2Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait
We address the problem of whole-body person recognition in unconstrained environments. This problem arises in surveillance scenarios such as those in the IARPA Biometric Recognition and Identification at Altitude and Ran…
Face RecognitionPerson RecognitionRTETAR+1