Multi-task aided face recognition network with convolution kernel spatial collaboration

Most face recognition networks based on convolutional neural networks are easily affected by nonlinear factors of expression and posture, and the single task cannot adapt to multi-task scenarios. In addition, there is still room for further utilization of convolution kernels. Therefore, this paper proposes a multi-task aided face recognition network with convolution kernel spatial collaboration. The network is based on GhostNet, and the auxiliary task branch of expression and posture is added, which reduces the influence of nonlinear factors of expression and posture on face recognition, and enables the network to recognize expression and posture so that the network can adapt to the situation of more tasks. On this basis, the convolution of convolution is improved to increase its generality on grouped convolution, and it is applied to the cheap operation of Ghost Module so that the network can further use the convolution kernel to learn additional feature maps. Finally, a multi-task feature fusion module is proposed, which combines the shared features extracted from the backbone network with the features of the branch network, and further utilizes the features between different tasks to improve the performance of each task. The proposed method was compared with the existing methods based on deep learning on different datasets. The experimental results show that the proposed method has better recognition performance in face recognition, and the model size and inference speed are close to the lightweight network, which is suitable for more tasks.