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An Auxiliary Method Based on Hyperspectral Reflectance for Presentation Attack Detection

Shiwei Li 1 Mohsen Ardabilian 1 Abdel-Malek Zine 2
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 MMCS - Modélisation mathématique, calcul scientifique
ICJ - Institut Camille Jordan [Villeurbanne]
Abstract : Face recognition has reached a high accuracy in recent years by adopting convolutional neural networks. However, it suffers from presentation attacks such as 2D face photos, and 3D masks. The vulnerability of face recognition and presentation attacks detection (PAD) attract numerous researchers in recent years. Most studies have only focused on PAD algorithms by analyzing texture information, depth information or thermal images. On the other hand, hyperspectral reflectance, which benefits from the development of line-scan HSI sensors, makes it possible to detect information about the inner structure of materials. Our research proposes an auxiliary method to support face recognition by analyzing hyperspectral reflectance. Combined with biological facts of human skin, we trained a neural network with pigmentation fractions inside human skin and corresponding reflectance. The results show high accuracy in identifying skin and non-skin.
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Submitted on : Friday, June 19, 2020 - 9:01:55 PM
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Shiwei Li, Mohsen Ardabilian, Abdel-Malek Zine. An Auxiliary Method Based on Hyperspectral Reflectance for Presentation Attack Detection. The 15th International Conference on Signal Image Technology & Internet based Systems, Nov 2019, Sorrento, Italy. ⟨10.1109/SITIS.2019.00042⟩. ⟨hal-02875939⟩



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