Cross Modal Focal Loss for RGBD Face Anti-Spoofing

TL;DR

This paper introduces a novel cross-modal focal loss for RGBD face anti-spoofing, enhancing generalization and robustness by effectively combining RGB and depth data while mitigating overfitting.

Contribution

The work proposes a new framework with a cross-modal focal loss function that dynamically balances RGB and depth information for improved presentation attack detection.

Findings

Effective in generalizing to unseen attacks

Improves robustness over existing methods

Validated on two public datasets

Abstract

Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing to unseen attacks. In recent years, multi-channel methods have been proposed to improve the robustness of PAD systems. Often, only a limited amount of data is available for additional channels, which limits the effectiveness of these methods. In this work, we present a new framework for PAD that uses RGB and depth channels together with a novel loss function. The new architecture uses complementary information from the two modalities while reducing the impact of overfitting. Essentially, a cross-modal focal loss function is proposed to modulate the loss contribution of each channel as a function of the confidence of individual channels. Extensive evaluations in two publicly available datasets demonstrate the effectiveness of the proposed approach.