A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Abstract Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of presenting a recaptured frame before the verification device to gain illegal access on behalf of a legitimate person with or without their concern. Several methods have been proposed to detect face spoofing attacks over the last decade. However, these methods only consider the luminance information, reflecting poor discrimination of spoofed face from the genuine face. This article proposes a practical approach combining Local Binary Patterns (LBP) and convolutional neural network-based transfer learning models to extract low-level and high-level features. This paper analyzes three color spaces (i.e., RGB, HSV, and YCrCb) to understand the impact of the color distribution on real and spoofed faces for the NUAA benchmark dataset. In-depth analysis of experimental results and comparison with other existing approaches show the superiority and effectiveness of our proposed models.