Deep learning based intelligent system for robust face spoofing detection using texture feature measurement

Abstract

The use of biometric structures in our everyday lives is becoming increasingly frequent. Biometrics play a crucial role in various applications, including crime analysis, person identification, and verification. Among different biometrics, the face provides a rich set of features. However, face spoofing is a continuous threat in real-world environments, leading to abnormal behavior in security systems. Conventional face spoofing analysis methods have often failed to achieve optimal performance in detecting face spoofing, faking, and attacks due to limitations in low-level feature extraction. To address this issue, this research introduces a novel technique called Face Spoofing Detection (FSD) based on Deep Learning Convolutional Neural Network (DLCNN), named NLBP-Net. In this technique, features from face images are extracted using Local Binary Pattern (LBP). The periocular area, which remains untouched by the gradient process, is a section of the human face that stands out as being highly unique. Furthermore, the extracted features are trained using the advanced Visual Geometry Group 16 (VGG16) methods. The trained model effectively classifies spoofing and faking attempts in random face images. Simulations conducted on a standard dataset demonstrate that the proposed NLBP-Net outperforms other methods across several metrics. NLBP-Net achieves an accuracy of 99.593%, sensitivity of 99.633%, specificity of 99.137%, recall of 99.224%, precision of 99.057%, and F1-score of 99.057%.