Reflection Analysis for Face Morphing Attack Detection

TL;DR

This paper proposes a novel face morphing attack detection method by analyzing inconsistencies in facial illumination and specular highlights, improving the ability to identify fraudulent morphs in biometric systems.

Contribution

It introduces a new approach that examines illumination consistency and specular highlights to detect face morphing attacks, addressing limitations of existing methods.

Findings

Effective detection of morphing attacks through highlight analysis

Inconsistencies in reflections indicate morphing presence

Method improves robustness against different morphing scenarios

Abstract

A facial morph is a synthetically created image of a face that looks similar to two different individuals and can even trick biometric facial recognition systems into recognizing both individuals. This attack is known as face morphing attack. The process of creating such a facial morph is well documented and a lot of tutorials and software to create them are freely available. Therefore, it is mandatory to be able to detect this kind of fraud to ensure the integrity of the face as reliable biometric feature. In this work, we study the effects of face morphing on the physically correctness of the illumination. We estimate the direction to the light sources based on specular highlights in the eyes and use them to generate a synthetic map for highlights on the skin. This map is compared with the highlights in the image that is suspected to be a fraud. Morphing faces with different geometries, a bad alignment of the source images or using images with different illuminations, can lead to inconsistencies in reflections that indicate the existence of a morphing attack.