University of Oulu

U. Muhammad and A. Hadid, "Face Anti-spoofing using Hybrid Residual Learning Framework," 2019 International Conference on Biometrics (ICB), Crete, Greece, 2019, pp. 1-7, doi: 10.1109/ICB45273.2019.8987283

Face anti-spoofing using hybrid residual learning framework

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Author: Muhammad, Usman1; Hadid, Abdenour2
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland
2Center for Machine Vision and Signal Analysis (CMVS) University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-06-24


Face spoofing attacks have received significant attention because of criminals who are developing different techniques such as warped photos, cut photos, 3D masks, etc. to easily fool the face recognition systems. In order to improve the security measures of biometric systems, deep learning models offer powerful solutions; but to attain the benefits of multilayer features remains a significant challenge. To alleviate this limitation, this paper presents a hybrid framework to build the feature representation by fusing ResNet with more discriminative power. First, two variants of the residual learning framework are selected as deep feature extractors to extract informative features. Second, the fullyconnected layers are used as separated feature descriptors. Third, PCA based Canonical correlation analysis (CCA) is proposed as a feature fusion strategy to combine relevant information and to improve the features’ discrimination capacity. Finally, the support vector machine (SVM) is used to construct the final representation of facial features. Experimental results show that our proposed framework achieves a state-of-the-art performance without finetuning, data augmentation or coding strategy on benchmark databases, namely the MSU mobile face spoof database and the CASIA face anti-spoofing database.

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Series: International Conference on Biometrics
ISSN: 2376-4201
ISSN-L: 2376-4201
ISBN: 978-1-7281-3640-0
ISBN Print: 978-1-7281-3641-7
Pages: 1 - 7
Article number: 8987283
DOI: 10.1109/ICB45273.2019.8987283
Host publication: 2019 International Conference on Biometrics, ICB 2019
Conference: International Conference on Biometrics
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: The financial support of the Academy of Finland is acknowledged.
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