Face Presentation Attack Detection via Ensemble Learning Algorithm
Kim Wang Lee, Jit Yan Lim, Kian Ming Lim, Chin Poo Lee · 2023
Face recognition systems are vulnerable to a variety of presentation assaults, including print, mask, and replay attacks. To successfully address the issues faced by these assaults, we offer a deep learning-based technique based on the VGG19, ResNet152, and DenseNet161 models in this study. We also investigate the ensemble learning bagging strategy to improve classification reliability further. The experimental findings show that our proposed strategy is successful at recognising and categorising presentation assaults. The ensemble learning approach significantly increases overall accuracy when compared with training each model independently, producing groundbreaking outcomes on the investigated datasets. Based on the results, we were able to propose bagging technique, which performed quite well in Replay-Attack and OULU-NPU with 1.22% and 4.86%, respectively.