Impact of Image Context for Single Deep Learning Face Morphing Attack Detection

TL;DR

This paper examines how image alignment affects deep learning-based face morphing attack detection, identifying optimal conditions to improve system robustness against manipulation techniques.

Contribution

It introduces an analysis of alignment settings and their influence on detection performance, providing guidelines for better face morphing attack detection.

Findings

Optimal alignment improves detection accuracy

Face contour and image context are crucial for detection performance

Guidelines for alignment settings enhance robustness

Abstract

The increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.