ArticleSensors (Basel, Switzerland)2022
Gaze Estimation Approach Using Deep Differential Residual Network.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Design of a Gaze-Controlled Interactive Art System for the Elderly to Enjoy Life.Sensors (Basel, Switzerland) · 2024Article
- FreeGaze: A Framework for 3D Gaze Estimation Using Appearance Cues from a Facial Video.Sensors (Basel, Switzerland) · 2023Article
- MEST: An Action Recognition Network with Motion Encoder and Spatio-Temporal Module.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
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Abstract
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. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with
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