Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features

TL;DR

This paper introduces two novel robust features for face spoofing detection, utilizing binocular depth and micro-texture analysis, and demonstrates their effectiveness through experiments on multiple datasets, outperforming existing methods.

Contribution

The paper proposes two new features, TFBD and SPMT, along with registration and coding algorithms, for improved multi-modal face spoofing detection.

Findings

Fusion of the proposed features enhances robustness and efficiency.

The method outperforms state-of-the-art traditional approaches.

Experiments validate effectiveness on multiple datasets.

Abstract

Robust features are of vital importance to face spoofing detection, because various situations make feature space extremely complicated to partition. Thus in this paper, two novel and robust features for anti-spoofing are proposed. The first one is a binocular camera based depth feature called Template Face Matched Binocular Depth (TFBD) feature. The second one is a high-level micro-texture based feature called Spatial Pyramid Coding Micro-Texture (SPMT) feature. Novel template face registration algorithm and spatial pyramid coding algorithm are also introduced along with the two novel features. Multi-modal face spoofing detection is implemented based on these two robust features. Experiments are conducted on a widely used dataset and a comprehensive dataset constructed by ourselves. The results reveal that face spoofing detection with the fusion of our proposed features is of strong robustness and time efficiency, meanwhile outperforming other state-of-the-art traditional methods.