A Deep Face Antispoofing System with Hardware Implementation for Real-Time Applications

A deep learning-based face anti-spoofing system has been proposed here. This work has been implemented in four segments. Firstly, an image preprocessing task is performed to extract the facial region. Then, the texture analysis of the facial region is performed to compute discriminant features. For this, a robust approach to deep learning techniques is needed, starting with defining some convolutional neural network (CNN) architectures for feature computation, followed by the classification of genuine vs. imposter face liveliness. The motivation of this work is to find both software- and hardware-based solutions to access biometric-based real-time systems through robust and vigorous face-liveness detection techniques. The recognition system’s performances are further improved by image acquisition-challenging issues, image augmentation, fine-tuning, transfer learning, and the fusion of various trained CNN models. Finally, the above steps have been embedded in Raspberry Pi devices to build the system for real-time applications. The experimentation with two benchmark databases, NUAA and CASIA Replay-Attack, and comparing the performance with some well-known methods relating to the proposed system area show the proposed system’s superiority.