On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection

2026-04-22

AI Summary

This study addresses the challenge that complex backgrounds in unconstrained scenarios—such as airport border control—degrade face recognition accuracy and impair the detection of presentation attacks. The authors systematically evaluate the impact of multiple face segmentation methods on four representative recognition models and three attack detection techniques through comprehensive experiments on datasets encompassing both controlled and unconstrained imagery. For the first time, they comprehensively demonstrate the dual role of background removal in simultaneously influencing recognition performance and security mechanisms, showing its significant effects on image quality, identification accuracy, and attack detectability. These findings provide empirical grounding and practical guidance for preprocessing strategies in real-world biometric systems, effectively bridging the critical gap between deployment feasibility and system reliability.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This study investigates the impact of face image background correction through segmentation on face recognition and morphing attack detection performance in realistic, unconstrained image capture scenarios. The motivation is driven by operational biometric systems such as the European Entry/Exit System (EES), which require facial enrolment at airports and other border crossing points where controlled backgrounds usually required for such captures cannot always be guaranteed, as well as by accessibility needs that may necessitate image capture outside traditional office environments. By analyzing how such preprocessing steps influence both recognition accuracy and security mechanisms, this work addresses a critical gap between usability-driven image normalization and the reliability requirements of large-scale biometric identification systems. Our study evaluates a comprehensive range of segmentation techniques, three families of morphing attack detection methods, and four distinct face recognition models, using databases that include both controlled and in-the-wild image captures. The results reveal consistent patterns linking segmentation to both recognition performance and face image quality. Additionally, segmentation is shown to systematically influence morphing attack detection performance. These findings highlight the need for careful consideration when deploying such preprocessing techniques in operational biometric systems.

Problem

• face segmentation
• background removal
• face recognition
• morphing attack detection
• unconstrained imaging

Innovation

• face segmentation
• background removal
• morphing attack detection
• face recognition
• in-the-wild imaging