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A Combined Feature-Based Framework for Disguise and Spoofing Detection in Face Recognition Systems — arXiv:2608.08521

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.08521
License (as archived): CC0-1.0
Archived: 2026-10-19T08:58:31.469Z
SHA-256 of the archived text: 7e423541c6fdd580f211f100851e3cc7b1ad7cf3bd4edfc2c896921e73bd6951
Stable URL of this page: https://metraipolis.com/documento/dc_4a9300a1cbe6

How to cite

A Combined Feature-Based Framework for Disguise and Spoofing Detection in Face Recognition Systems — arXiv:2608.08521 — arXiv (en), https://arxiv.org/abs/2608.08521 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4a9300a1cbe6 (Metraipolis archive dc_4a9300a1cbe6, sha256 7e423541c6fdd580f211f100851e3cc7b1ad7cf3bd4edfc2c896921e73bd6951)

BibTeX

@misc{dc4a9300a1cbe6,
  title = {A Combined Feature-Based Framework for Disguise and Spoofing Detection in Face Recognition Systems — arXiv:2608.08521},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.08521},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4a9300a1cbe6; sha256 7e423541c6fdd580f211f100851e3cc7b1ad7cf3bd4edfc2c896921e73bd6951},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.08521. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Face recognition systems face two distinct, commonly-separated failure modes: spoofing, where an impostor presents a photograph or video of an authorized user, and disguise, where a legitimate user is rejected because their appearance differs from their enrolled template due to accessories, facial hair, illumination, or pose. This paper proposes and compares five combined feature-extraction and classification pipelines that address both problems within a single framework: PM (PCA and Minimum Euclidean Distance, MED), LPM (Local Binary Patterns with PCA and MED), HPM (Histogram of Oriented Gradients with PCA and MED), SM (Speeded-Up Robust Features with MED), and HM (Harris corner features with MED). Each pipeline follows a common two-phase process comprising pre-processing, feature extraction, feature filtering, and classification. The methods were trained on 115 subjects drawn from the FEI, Disguised Faces Database, and NUAA databases and evaluated on six test conditions covering mixed appearances, frontal faces, dark illumination, left- and right-turned poses, and photo-spoofing attempts. The HOG-based pipeline (HPM) achieved the most consistent performance across conditions, with 94.59% accuracy on mixed-appearance disguise, 81.5-93.2% across pose and illumination variants, and 91.67% on spoofing, while the LBP-based pipeline (LPM) achieved the second-highest spoofing-detection accuracy (93.2%), behind PM (96.67%), but weaker robustness to pose change. These results reveal a measurable trade-off between spoof sensitivity and disguise robustness among classical feature representations, motivating the deep-learning and cross-database extensions discussed in the concluding sections.

Tema degli esploratori: fiducia digitale e crittografia (retro-catalogo arXiv: annunci del 2026-08-11 in cs.CR).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2608.08521 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:54:24.992Z dalla pagina https://arxiv.org/catchup/cs.CR/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).