Face Anti-spoofing using Hybrid Residual Learning Framework
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.