Data augmentation based face anti-spoofing (FAS) scheme using deep learning techniques

Recent years have seen a rise in interest in face anti-spoofing (FAS) owing to the critical function it plays in protecting face recognition systems against presentation assaults (PAs). Early-stage FAS approaches relying on handmade characteristics become inaccurate when steadily realistic PAs of unique sorts emerge. Thus, face anti-spoofing algorithms are gaining increasing relevance in such setups. A very innovative method called deep learning has shown remarkable success in difficult computer vision problems. The proposed method uses deep acquisition and transfer of learning to extract characteristics from people's faces. This is why the authors of this study recommend using the Faster RCNN classifier with a face-liveness detection approach. Two distinct components-the data augmentation module for assessing sparse information as well as the faster RCNN classifier module-make up the anti-spoofing approach. We may use any publicly accessible dataset to train our quicker RCNN classifier. We successively fused these two components and used the Android platform to create a basic face recognition app. The results of the tests demonstrate that the developed module can identify several types of face spoof assaults, such as those carried out with the use of posters, masks, or cell phones. Testing the proposed architecture both across and inside databases using three benchmarking (Idiap Replay Attack, CASIA- FASD, & 3DMAD) demonstrate its ability to deliver outcomes on par with cutting-edge techniques.