Vol.70, No.3, 2022, pp.5107-5132, doi:10.32604/cmc.2022.021107
OPEN ACCESS
ARTICLE
Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction
  • Jung-Hwa Kim1, Jin-Woo Jeong2,*
1 Department of Computer Engineering, Kumoh National Institute of Technology, Gumi, 39177, Korea
2 Department of Data Science, Seoul National University of Science and Technology, Seoul, 01811, Korea
* Corresponding Author: Jin-Woo Jeong. Email:
Received 23 June 2021; Accepted 07 August 2021; Issue published 11 October 2021
Abstract
Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users’ continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze direction. The proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned conditions. To this end, we implemented a deep learning pipeline that can handle different types and combinations of data. The proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze estimators. Through various experiments, we found that 1) an infrared-modality provides more useful features than a depth-modality, 2) multi-view multi-modal approaches provide better accuracy than single-view single-modal approaches, and 3) the proposed estimators achieve a high inference efficiency that can be used in real-time applications.
Keywords
Human-computer interaction; deep learning; head-gaze estimation; indoor monitoring
Cite This Article
Kim, J., Jeong, J. (2022). Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction. CMC-Computers, Materials & Continua, 70(3), 5107–5132.
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