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In addition, we proposed a new loss function for the gaze estimation. We combined the original gaze direction and the difference information through the shortcut-connection in DRNet. We firstly treated the difference information as auxiliary information in the proposed DRNet. Several other works mention that the performance based on the methods considering the difference information is directly affected by the number and the specific label of the inference image. developed Diff-Nn for the gaze estimation using the left and right eye patch of one face simultaneously. propose a differential network (Diff-Nn) to address the gaze calibration problem by directly predicting the difference information between two images of the eyes. However, this solution requires many inference images with the label. An effective and simple solution proposed by many publications to solve this problem is to adjust the weight of the model after training. Gaze calibration problem also limits the performances of CNNs. ( Bottom) Left- and right-eye images are captured from the original RGB frames. ( a, b) are two example frames of the dataset. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images.Įxample of noise images in Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with angular-error of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. We treat the difference information as auxiliary information.

We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. However, this solution results in a loss of accuracy when using only one inference image. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain.

Gaze estimation, which is a method to determine where a person is looking at given the person’s full face, is a valuable clue for understanding human intention.

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