Federated Multitask Learning with Manifold Regularization for Face Spoof Attack Detection∗

Yingyue Chen, Liang Chen, Chaoqun Hong, Xiaodong Wang and Paolo Spagnolo

Mathematical Problems in Engineering, 2022, vol. 2022, 1-10

Abstract: Face recognition has been widely used in personal authentication, especially on edge computing devices. However, face recognition systems suffer from face spoof attack. In this paper, a novel method for face spoof attack detection in edge computing scenarios is proposed. It is based on federated learning and improves traditional federated learning with multitask learning and manifold regularization, which is known as federated learning for face spoof attack detection (FedFSAD). In this way, local model learning is completed on edge devices and global model learning only depends on the trained local models without using the original image data. Besides, the performance is improved by imposing hypergraph manifold regularization in the global training of multitask learning. The results of comprehensive experiments show that the detection performance is improved by about 10% and robust against stragglers and network delays, which indicates the effectiveness of FedFSAD.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/7759410.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/7759410.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7759410

DOI: 10.1155/2022/7759410

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().