A deep face spoof detection framework using multi-level ELBPs and stacked LSTMs

Facial recognition technology has emerged as the most important element in many interactive AI systems due to its ease and accuracy on par with that of humans. Nevertheless, its dependable deployment is constrained by vulnerability to presentation attacks. Therefore, for facial recognition technology to be used safely in unsupervised situations, automatic detection of presentation attacks is crucial. In this context, we propose a novel deep face spoof detection framework, which employs multi-level Elliptical Local Binary Pattern (ELBP) and stacked LSTMs. The ELBP, a variant of Local Binary Patterns (LBPs), is utilized in three levels for three color spaces—RGB, HSV, and RGB + HSV—to acquire discriminating features. We evaluate our framework through extensive experiments on two publicly available and challenging datasets—CASIA-FASD, CASIA-SURF, and OULU-NPU. The experimental results demonstrate that our framework achieves better performance in terms of APCER, NPCER, ACER, EER, ROCs, and confusion matrix.