Name
Abstract | ||
|---|---|---|
The face liveness detection module is one of the most important parts in the state-of-the-art face recognition system. In this paper, we present an efficient method to further improve its accuracy by leveraging multiple feature descriptors. Firstly, a data-driven feature descriptor is proposed based on the Karhunen-Loève Transform (KLT) learned from both client and imposter face images. Moreover, the Completed Local Binary Pattern (CLBP) algorithm is utilized to represent the local structure and the high-middle spectra components of 2D Fourier transform are also utilized to reflect the global structure. These features are fed into the support vector machine (SVM) to learn a classifier for face liveness detection. Experimental results on NUAA illustrate that our proposed method outperforms most of the widely utilized feature descriptors. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TAAI48200.2019.8959844 | 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) |
Keywords | DocType | ISSN |
Face liveness detection,difference of Gaussian,2D Fourier Spectra,CLBP,KLT | Conference | 2376-6816 |
ISBN | Citations | PageRank |
978-1-7281-4667-6 | 0 | 0.34 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjing Li | 1 | 597 | 44.26 |
| Zhang X | 2 | 250 | 34.16 |
| Yongbing Zhang | 3 | 0 | 0.34 |
| Wang H | 4 | 71 | 29.35 |
| Fang Yang | 5 | 0 | 0.34 |