LIGHTEWIGHT FACE ANTI-SPOOFING ALGORITHM BASED ON IMPROVED MOBILENETV3
In order to solve the problem of fraud attacks in face recognition systems and the problem that most deep learning based in-vivo detection methods use large convolutional networks as the backbone network,which leads to complex model structure and large computation amount,an improved lightweight face detection algorithm based on MobileNetV3 is proposed.This paper discussed the shortcomings of using global average pooling to calculate channel attention weight in MobileNetV3 and using double nonlinear fully connected layer.A new attention mechanism EFCANet was proposed,and the EFCANet network was used to improve MobileNetV3 lightweight convolutional neural network.The experimental results show that the improved light-weight face anti-spoofing algorithm has a good performance in detection accuracy,network model size,loss value,and equal error rate.
万方数据
维普
