Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Information

TL;DR

This paper presents a method for face morphing attack detection that uses feature selection based on mutual information to identify key facial areas, improving detection accuracy and interpretability.

Contribution

It introduces a mutual information-based feature selection process to enhance morphing attack detection and localize critical facial regions for manual and automatic analysis.

Findings

Optimal features are selected using Conditional Mutual Information.

Eyes and nose are identified as critical regions for detection.

High detection accuracy achieved with reduced feature sets.

Abstract

Face morphing attack detection is a challenging task. Automatic classification methods and manual inspection are realised in automatic border control gates to detect morphing attacks. Understanding how a machine learning system can detect morphed faces and the most relevant facial areas is crucial. Those relevant areas contain texture signals that allow us to separate the bona fide and the morph images. Also, it helps in the manual examination to detect a passport generated with morphed images. This paper explores features extracted from intensity, shape, texture, and proposes a feature selection stage based on the Mutual Information filter to select the most relevant and less redundant features. This selection allows us to reduce the workload and know the exact localisation of such areas to understand the morphing impact and create a robust classifier. The best results were obtained for the method based on Conditional Mutual Information and Shape features using only 500 features for FERET images and 800 features for FRGCv2 images from 1,048 features available. The eyes and nose are identified as the most critical areas to be analysed.