Machine Learning Model for Face Spoof Detection with Textural Feature Extraction

Face spoof detection is a scheme for detecting the spoofed faces. Various techniques have been designed for the face spoof detection, based on features, machine learning (ML) and deep learning (DL) in the past few years, and has drawn great interest in a variety of domains that are most accessible and leads to maintain abundant information in day-to-day life. Face spoof detection techniques which are based on Artificial Intelligence (AI) contain several stages such as pre-process the data, extract the features and classify the faces. The earlier method makes the deployment of K-Nearest Neighbor with Grey Level Co-occurrence Matrix to detect the face spoofing, but at lower accuracy. Moreover,
$\text{DWT}-\text{LBP}-\text{DCT}+\text{SVM}$
algorithm is also implemented to detect the face-spoofing. However, the accuracy of these algorithms is found lower. This work projects a hybrid technique in which RF, K-Nearest Neighbor (KNN) and Support Vector Machine algorithms are put together. The simulation results exhibited the supremacy of the projected technique as compared to other methods concerning accuracy.