基于差分卷积的自适应视线估计

文章提出了一种基于差分卷积神经网络的自适应视线估计模型。在模型中,融入头部姿态信息,利用差分卷积设计了一种差分网络(Differential Network,DNet),通过训练该网络来预测眼睛的凝视差异,用以校准初步视线估计结果,进而降低视线估计误差。通过在公开数据集Eyediap上进行验证,并与其他性能良好的视线估计模型进行比较,结果均表明所提出的视线估计模型在头部自由运动的状态下可以更准确地估计视线方向。

Adaptive Gaze Estimation Based on Differential Convolution

An adaptive model is proposed for gaze estimation based on differential convolutional neural network. The adaptive model incorporates information of head pose and designs a network named Differential Network (DNet) by virtue of differential convolution. The DNet is trained to predict gaze differences in the eyes, calibrate the initial gaze estimations and thus reduce the estimation errors. Through validation on the publicly available dataset Eyediap and comparison with other well-performed gaze estimation models developed in recent years, the experimental results indicate that the proposed adaptive model can estimate gaze directions more accurately under free head movement.