Attention-Based Face AntiSpoofing of RGB Images, using a Minimal End-2-End Neural Network
This addresses security-sensitive applications like liveness detection, but it is incremental as it adapts existing architectures.
The paper tackles face anti-spoofing by proposing two end-to-end convolutional neural network systems, one based on EfficientNet B0 and another lightweight model based on MobileNet V2, achieving remarkable results in detecting real and fake face images.
Face anti-spoofing aims at identifying the real face, as well as the fake one, and gains a high attention in security-sensitive applications, liveness detection, fingerprinting, and so on. In this paper, we address the anti-spoofing problem by proposing two end-to-end systems of convolutional neural networks. One model is developed based on the EfficientNet B0 network which has been modified in the final dense layers. The second one, is a very light model of the MobileNet V2, which has been contracted, modified and retrained efficiently on the data being created based on the Rose-Youtu dataset, for this purpose. The experiments show that, both of the proposed architectures achieve remarkable results on detecting the real and fake images of the face input data. The experiments clearly show that the heavy-weight model could be efficiently employed in server-side implementations, whereas the low-weight model could be easily implemented on the hand-held devices and both perform perfectly well using merely RGB input images.