Back to Search
Start Over
UCDCN: a nested architecture based on central difference convolution for face anti-spoofing.
- Source :
- Complex & Intelligent Systems; Aug2024, Vol. 10 Issue 4, p4817-4833, 17p
- Publication Year :
- 2024
-
Abstract
- The significance of facial anti-spoofing algorithms in enhancing the security of facial recognition systems cannot be overstated. Current approaches aim to compensate for the model's shortcomings in capturing spatial information by leveraging spatio-temporal information from multiple frames. However, the additional branches to extract inter-frame details increases the model's parameter count and computational workload, leading to a decrease in inference efficiency. To address this, we have developed a robust and easily deployable facial anti-spoofing algorithm. In this paper, we propose Central Difference Convolution UNet++ (UCDCN), which takes advantage of central difference convolution and improves the characterization ability of invariant details in diverse environments. Particularly, we leverage domain knowledge from image segmentation and propose a multi-level feature fusion network structure to enhance the model's ability to capture semantic information which is beneficial for face anti-spoofing tasks. In this manner, UCDCN greatly reduces the number of model parameters as well as achieves satisfactory metrics on three popular benchmarks, i.e., Replay-Attack, Oulu-NPU and SiW. [ABSTRACT FROM AUTHOR]
- Subjects :
- HUMAN facial recognition software
IMAGE segmentation
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 21994536
- Volume :
- 10
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Complex & Intelligent Systems
- Publication Type :
- Academic Journal
- Accession number :
- 178504553
- Full Text :
- https://doi.org/10.1007/s40747-024-01397-0