TL;DR
The experimental results demonstrate that the proposed framework outperforms existing methods in intra and cross-dataset evaluation while maintaining a compact model size, indicating the strong generalization capability of the proposed approach across different datasets with minimal model size.
A multi-dimensional feature fusion-based face anti-spoofing detection method based on the YOLOv8 architecture that integrates dynamic optical flow features with static texture analysis and achieves robust detection through the fusion of spatial and temporal cues.
Biometric face recognition systems are progressively being used in security-sensitive areas and are vulnerable to presentation attacks (PAs) such as printed photograph recaptures, video replay attacks and 3D silicone mask attacks. Face Anti-Spoofing (FAS) also known as liveness detection is the first line of defence ag...
Face anti-spoofing (FAS) is vital for safeguarding biometric systems against presentation attacks, including photos, videos, or masks aimed at impersonating legitimate users. This paper presents an efficient multi-modal face liveness detection method that combines RGB, depth, and infrared (IR) images through a lightwei...
Results show that the privacy preserving method introduced in this paper can effectively remove identity-related visual signals while at the same time keeping the level of face detection performance high, a feature which would make it most of all appealing for privacy critical computer vision applications.
The proposed Adaptive Multimodal Liveness Detection Framework provides a highly effective means to improve the capability of next generation biometric authentication systems, particularly in an edge computing environment.
The evolution of sophisticated generative artificial intelligence has led to the rapid development of very realistic manipulated images and videos, posing substantial risks for digital trust, cyber security, and multimedia authenticity. Advanced Deepfake generation technologies result in the creation of believable forg...
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