Abstract
As a method of predicting the target's attention distribution, gaze estimation plays an important role in human-computer interaction. In this paper, we learn a 3D gaze estimator with adaptive weighted strategy to get the mapping from the complete images to the gaze vector. We select the both eyes, the complete face and their fusion features as the input of the regression model of gaze estimator. Considering that the different areas of the face have different contributions on the results of gaze estimation under free head movement, we design a new learning strategy for the regression net. To improve the efficiency of the regression model to a great extent, we propose a weighted network that can adjust the learning strategy of the regression net adaptively. Experimental results conducted on the MPIIGaze and EyeDiap datasets demonstrate that our method can achieve superior performance compared with other state-of-the-art 3D gaze estimation methods.
Highlights
The gaze vector can be speculated from the pupil to the target’s attention
In order to further utilize the powerful function of Convolutional Neural Network (CNN) and improve the accuracy of gaze vector prediction, we propose an adaptive weighted 3D gaze estimation method
(3) We propose a weighted network to judge the contribution of face, left eye and right eye images on the results of gaze estimation
Summary
The gaze vector can be speculated from the pupil to the target’s attention. It has been increasingly important as a non-verbal cue in many fields, including marketing and consumer research [1], [2], human-computer interaction [3]–[5], medical care [6]–[8], aviation and vehicle driving [9], and criminal investigation [10]–[12]. With the development of machine learning and the support of massive data, extensive learning-based gaze estimation methods have been presented These methods such as Convolutional Neural Network (CNN)-based methods, have great potential to handle the challenges faced by traditional methods, including redundancy calibration process, complex head postures, and limitation of lighting conditions. Wang et al [36] proposed to combine adversarial learning and Bayesian inference into a unified framework They added an antagonistic component to traditional CNN-based gaze estimators so they could learn features that respond to the gaze. In order to further utilize the powerful function of CNNs and improve the accuracy of gaze vector prediction, we propose an adaptive weighted 3D gaze estimation method. The adaptive weighting is realized by adjusting the strategy of regression model by weight value
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