@inproceedings{f649e3da2d284cf6890c583b324f617b,
title = "Face Anti-Spoofing Detection Using Structure-Texture Decomposition",
abstract = "A key area in computer vision and biometric authentication systems is detecting and classifying face anti-spoofing. The novel method for face anti-spoofing presented in this paper focuses on color invariant methods. The proposed approach consists to compare two different feature extraction methods: LBP and HOG, used with two different machine-learning models such as KNN and SVM. The suggested method improves the system's ability to discriminate by utilizing color properties and overcoming obstacles like varying lighting and image quality. The structure-texture decomposition is used as a feature that is used to improve the performance of the face anti-spoofing methods. After the experimental results using different techniques, we noticed that structure-texture decomposition can be a good feature for face anti-spoofing detection features.",
keywords = "Face-spoofing, HOG method, KNN model, LBP method, SVM model",
author = "Dareen Douglas and {Ben Hassen}, Nada and Asmaa Aslam and Omar Elharrouss and Somaya Al-Maadeed",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 ; Conference date: 23-10-2023 Through 26-10-2023",
year = "2023",
doi = "10.1109/ISNCC58260.2023.10323852",
language = "English",
series = "2023 International Symposium on Networks, Computers and Communications, ISNCC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Symposium on Networks, Computers and Communications, ISNCC 2023",
}