A Patch-Based CNN Built on the VGG-16 Architecture for Real-Time Facial Liveness Detection

Facial recognition is a prevalent method for biometric authentication that is utilized in a variety of software applications. This technique is susceptible to spoofing attacks, in which an imposter gains access to a system by presenting the image of a legitimate user to the sensor, hence increasing the risks to social security. Consequently, facial liveness detection has become an essential step in the authentication process prior to granting access to users. In this study, we developed a patch-based convolutional neural network (CNN) with a deep component for facial liveness detection for security enhancement, which was based on the VGG-16 architecture. The approach was tested using two datasets: REPLAY-ATTACK and CASIA-FASD. According to the results, our approach produced the best results for the CASIA-FASD dataset, with reduced HTER and EER scores of 0.71% and 0.67%, respectively. The proposed approach also produced consistent results for the REPLAY-ATTACK dataset while maintaining balanced and low HTER and EER values of 1.52% and 0.30%, respectively. By adopting the suggested enhanced liveness detection, architecture that is based on artificial intelligence could make current biometric-based security systems more secure and sustainable while also reducing the risks to social security.