Deep architecture-based face spoofing identification in real-time application
by Mayank Kumar Rusia; Dushyant Kumar Singh
International Journal of Biometrics (IJBM), Vol. 15, No. 2, 2023

Abstract: Face biometric-based recognition is always a demanding and universally accepted method, especially for access control purposes. However, face recognition systems are usually affected by identity threats, such as face spoofing. Face spoofing is an attempt to acquire the face identity privilege of another person illegally. Developing an efficient and real-time spoofing detection system that quickly detects any illegal access attempts to prevent vulnerability violations is indispensable. This manuscript proposes an automated and efficient technique for face spoofing detection based on a customised convolutional neural network named SpoofNET. The proposed model can easily distinguish genuine, and spoof faces with less complex convolutional blocks. This novel method can be deployed in any application that desires low computation and low-resolution input samples. This manuscript also introduces the dataset synthesised in our lab, validated with the existing NUAA dataset. The proposed model achieved 99.3% validation accuracy with better generalisation potentiality for the synthesised dataset.

Online publication date: Wed, 01-Mar-2023

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