Discriminative Representation Combinations for Accurate Face Spoofing Detection

TL;DR

This paper introduces three discriminative face spoofing detection features and combines them using decision-level and score fusion strategies, achieving superior results on multiple datasets.

Contribution

The paper proposes novel local micro-texture, deep learning, and stereo structure features for face spoofing detection, along with effective combination strategies.

Findings

Outperforms state-of-the-art methods on three public datasets.

Effective combination of local, deep, and stereo features enhances detection accuracy.

Achieves excellent performance on a new dataset.

Abstract

Three discriminative representations for face presentation attack detection are introduced in this paper. Firstly we design a descriptor called spatial pyramid coding micro-texture (SPMT) feature to characterize local appearance information. Secondly we utilize the SSD, which is a deep learning framework for detection, to excavate context cues and conduct end-to-end face presentation attack detection. Finally we design a descriptor called template face matched binocular depth (TFBD) feature to characterize stereo structures of real and fake faces. For accurate presentation attack detection, we also design two kinds of representation combinations. Firstly, we propose a decision-level cascade strategy to combine SPMT with SSD. Secondly, we use a simple score fusion strategy to combine face structure cues (TFBD) with local micro-texture features (SPMT). To demonstrate the effectiveness of our design, we evaluate the representation combination of SPMT and SSD on three public datasets, which outperforms all other state-of-the-art methods. In addition, we evaluate the representation combination of SPMT and TFBD on our dataset and excellent performance is also achieved.