.png){kind=icon}
Return
Diffusion-based kernel matrix model for face liveness detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2019.06.009.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Face recognition and verification systems are vulnerable to video spoofing attacks. In this paper, we present a diffusion-based kernel matrix model for face liveness detection. We use the anisotropic diffusion to enhance the edges of each frame in a video, and the kernel matrix model to extract the video features which we call the diffusion kernel (DK) features. The DK features reflect the inner correlation of the face images in the video. We employ a generalized multiple kernel learning method to fuse the DK features and the deep features extracted from convolution neural networks to achieve better performance. Our experimental evaluation on two publicly available datasets shows that the proposed method outperforms the state-of-art face liveness detection methods. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Face liveness detection
Anisotropic diffusion
Kernel matrix model
DK feature
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
4.1K
Citations:
6.7K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave
ShareSave
View more