基于ShuffleNet V2算法的三维视线估计

为了解决当前视线估计网络复杂度较深、精度不高的问题，同时为了未来将网络部署在移动设备端，提出了一种基于ShuffleNet V2算法的视线估计网络，其由脸部和眼睛两个子网络构成。脸部子网络通过ResNetV2网络对脸部图片进行特征处理，并加入人脸对齐算法，减少头部角度误差的影响。眼睛子网络通过ShuffleNet V2...

3D Gaze Estimation Based on ShuffleNet V2 Algorithm

In order to solve the problems of deep complexity and low accuracy of the current line of sight estimation network and to deploy the network on mobile devices in the future, we propose a line of sight estimation network based on ShuffleNet V2 algorithm, which consists of two sub-networks, face and eye. The face sub-network is used by ResNet V2 network. feature processing of face images and adding face alignment algorithm to reduce the effect of head angle error. The eye sub-network performs parallel feature processing of the eye images through ShuffleNet V2 and ResNet V2 algorithms. The network processes the feature images to get the angle parameters, and finally the angle of view is obtained by coordinate transformation. And experiments were conducted on the MPIIGaze dataset. The algorithm is improved for the lack of accuracy, and the attention mechanism (point-by-point square operation module) is added to ShuffleNet V2, and the verification experiments of the improved algorithm are conducted, and finally the experiments are compared with various advanced algorithms. The experiments show that the improved algorithm has higher accuracy than other algorithms.