Low-resolution face recognition based on super-resolution reconstruction and common feature subspace

Aiming at the low accuracy of traditional low-resolution face recognition, a low-resolution face recognition network InGLRNet (inception GAN low resolution Net) based on super-resolution reconstruction and common feature subspace is proposed. The InGLRNet network adopts a generative adversarial network structure. First, the generation network is composed of the super-resolution InSRNet network and the feature extraction network. The InSRNet network decomposes the 3×3 convolution kernel in the Inception structure into 1×3, 3×1 and 1×1 convolution kernels, and at the same time increases the bypass direct connection of residual network, which can alleviate the problem of gradient disappearance. Secondly, using the common feature subspace method, the distance between the transformed low-resolution sample image and the high-resolution reference image in the common space is used as the objective function for training a deep convolutional neural network, and the loss function is used to match the high and low features of high-resolution images to achieve accurate feature recognition of faces. Finally, the InGLRNet is compared with the four classic low-resolution face recognition methods, CLPMs, MDS, Deep-Face and Face-Net. The experimental results show that the constructed network has a significant improvement in face recognition performance. It is better than the other 4 methods at different low resolution.