Face Authentication from Grayscale Coded Light Field

TL;DR

This paper introduces a fast, reconstruction-free face authentication method using grayscale coded light field imaging, enhancing anti-spoofing and verification accuracy without requiring expensive 3D sensors.

Contribution

It proposes a novel grayscale coded light field system with a direct anti-spoofing mechanism and a multi-view verification network, reducing reliance on costly depth sensors.

Findings

Achieves competitive verification results with grayscale and low-res depth data.

Demonstrates effectiveness on simulated and real light field face datasets.

Provides a public dataset for further research.

Abstract

Face verification is a fast-growing authentication tool for everyday systems, such as smartphones. While current 2D face recognition methods are very accurate, it has been suggested recently that one may wish to add a 3D sensor to such solutions to make them more reliable and robust to spoofing, e.g., using a 2D print of a person's face. Yet, this requires an additional relatively expensive depth sensor. To mitigate this, we propose a novel authentication system, based on slim grayscale coded light field imaging. We provide a reconstruction free fast anti-spoofing mechanism, working directly on the coded image. It is followed by a multi-view, multi-modal face verification network that given grayscale data together with a low-res depth map achieves competitive results to the RGB case. We demonstrate the effectiveness of our solution on a simulated 3D (RGBD) version of LFW, which will be made public, and a set of real faces acquired by a light field computational camera.