Face Spoofing Detection on Low-Power Devices Using Embeddings with Spatial and Frequency-Based Descriptors

Abstract

A face spoofing attack occurs when an intruder attempts to impersonate someone with a desirable authentication clearance. To detect such intrusions, many researchers have dedicated their efforts to study visual liveness detection as the primary indicator to block spoofing violations. In this work, we contemplate low-power devices through the combination of Fourier transforms, different classification methods, and low-level feature descriptors to estimate whether probe samples correspond to spoofing attacks. The proposed method has low-computational cost and, to the best of our knowledge, this is the first approach associating features extracted from both spatial and frequency domains. We conduct experiments with embeddings of Support Vector Machines and Partial Least Squares on recent and well-known datasets under same and cross-database settings. Results show that, even though devised towards resource-limited single-board computers, our approach is able to achieve significant results, outperforming state-of-the-art methods.