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Data-Fusion-Based Two-Stage Cascade Framework for Multimodality Face Anti-Spoofing
- Source :
- IEEE Transactions on Cognitive and Developmental Systems. 14:672-683
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Existing face anti-spoofing models using deep learning for multi-modality data suffer from low generalization in the case of using variety of presentation attacks such as 2D printing and high-precision 3D face masks. One of the main reasons is that the non-linearity of multi-spectral information used to preserve the intrinsic attributes between a real and a fake face are not well extracted. To address this issue, we propose a multi-modility data based two-stage cascade framework for face anti-spoofing. The proposed framework has two advantages. Firstly, we design a two-stage cascade architecture that can selectively fuse low-level and high-level features from different modalities to improve feature representation. Secondly, we use multi-modality data to construct a distance-free spectral on RGB and infrared (IR) to augment the non-linearity of data. The presented data fusion strategy is different from popular fusion approaches, since it can strengthen discrimination ability of network models on physical attribute features than identity structure features under certain constraints. In addition, a multi-scale patch based weighted fine-tuning strategy is designed to learn each specific local face region. Experimental results show that the proposed framework achieves better performance than other state-of-the-art methods on both benchmark datasets and self-established datasets, especially on multi-material masks spoofing.
- Subjects :
- Spoofing attack
Computer science
business.industry
Deep learning
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Construct (python library)
Sensor fusion
Artificial Intelligence
Feature (computer vision)
Face (geometry)
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
Representation (mathematics)
business
Software
Subjects
Details
- ISSN :
- 23798939 and 23798920
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cognitive and Developmental Systems
- Accession number :
- edsair.doi...........cd7748568b41ef4f5b6a07a93f59074f