Detection of Facial Spoofing Attacks in Uncontrolled Environments using ELBP and Color Models

Abstract

Distance education has become an alternative in teaching derived from the Covid-19 pandemic. However, distance education has led to bad practices for some students. For example, it was detected that some students spoofed the teacher in a class or exam. Therefore, facial biometrics can be used to solve, in real-time, the spoofing problem. However, the solution is not exempt from presentation attacks that undermine the reliability of the systems. Other challenges that must be considered are lighting, resolution, and variable size of the faces, among others. In this paper, we present a methodology to address the problem of facial spoofing attacks. We combine the Extended Local Binary Patterns (ELBP) descriptor and YCbCr, HSV color models to highlight the saturation and illumination of an image. For the experiments, we present a comparison of our proposal against other state-of-the-art methods. We obtain an error of 2.45% with the Half Total Error Rate (HTER) metric in the MSU image bank. The results revealed that for environments where the camera resolutions are not controlled, our proposal provides a feasible solution reducing the costs of acquiring specific hardware.