Open AccessHybrid Algorithm for Face Spoof Detection
Author(s) -
Anupama Mittal,
Pravneet Kaur,
Ashish Oberoi
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40452
Subject(s) - spoofing attack , computer science , support vector machine , artificial intelligence , face (sociological concept) , pattern recognition (psychology) , classifier (uml) , random forest , feature extraction , algorithm , computer security , social science , sociology
The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that the introduced method performs more efficiently in comparison with the conventional techniques with regard to accuracy. Keywords: Face Spoof, KNN, Hybrid Classifier, GLCM