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

• URL: http://arxiv.org/abs/2604.20585v1
• Date: Wed, 22 Apr 2026 14:02:30 GMT
• Title: On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection
• Abstract summary: 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.<n>The motivation is driven by operational biometric systems such as the European Entry/Exit System (EES)<n>This work addresses a critical gap between usability-driven image normalization and the reliability requirements of large-scale biometric identification systems.
• Score: 17.17143165578033
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face [4.395715188789422]
  Face morphing attacks are one of the most challenging threats to face recognition systems used in electronic identity documents.<n>We propose a novel face morphing technique based on Arc2Face, an identity-conditioned face foundation model.<n> Experimental results show that the proposed deep learning-based approach achieves a morphing attack potential comparable to that of landmark-based techniques.
  arXiv  Detail & Related papers  (2026-02-18T16:11:11Z)
• Robust Deepfake Detection for Electronic Know Your Customer Systems Using Registered Images [29.349824933680956]
  We present a deepfake detection algorithm specifically designed for electronic Know Your Customer (eKYC) systems.<n>Our approach evaluates the video's authenticity by detecting temporal inconsistencies in identity vectors extracted by face recognition models.<n>In addition to processing video input, the algorithm utilizes a registered image (assumed to be genuine) to calculate identity discrepancies.
  arXiv  Detail & Related papers  (2025-07-30T12:16:27Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks [6.492755549391469]
  Face recognition systems are widely deployed in high-security applications.<n>Digital manipulations, such as face morphing, pose a security threat to face recognition systems.<n>We present a novel method for adapting deep learning-based face recognition systems to be more robust against face morphing attacks.
  arXiv  Detail & Related papers  (2024-01-21T21:04:05Z)
• Effective Adapter for Face Recognition in the Wild [72.75516495170199]
  We tackle the challenge of face recognition in the wild, where images often suffer from low quality and real-world distortions. Traditional approaches-either training models directly on degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective. We propose an effective adapter for augmenting existing face recognition models trained on high-quality facial datasets.
  arXiv  Detail & Related papers  (2023-12-04T08:55:46Z)
• Analyzing eyebrow region for morphed image detection [4.879461135691896]
  The proposed method is based on analyzing the frequency content of the eyebrow region. The findings suggest that the proposed method can serve as a valuable tool in morphed image detection.
  arXiv  Detail & Related papers  (2023-10-30T06:11:27Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• Impact of Image Context for Single Deep Learning Face Morphing Attack Detection [0.0]
  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.
  arXiv  Detail & Related papers  (2023-09-01T15:57:24Z)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning [59.548960057358435]
  We propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities. Our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities.
  arXiv  Detail & Related papers  (2022-07-08T09:23:59Z)
• Detect and Locate: A Face Anti-Manipulation Approach with Semantic and Noise-level Supervision [67.73180660609844]
  We propose a conceptually simple but effective method to efficiently detect forged faces in an image. The proposed scheme relies on a segmentation map that delivers meaningful high-level semantic information clues about the image. The proposed model achieves state-of-the-art detection accuracy and remarkable localization performance.
  arXiv  Detail & Related papers  (2021-07-13T02:59:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.