Bibliographic Details
| Title: |
Lightweight Geometric Framework for High-Precision 3D Gaze Tracking Based on Infrared Image Processing. |
| Authors: |
Shen J; School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China., Dong P; School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China., Hu B; School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China., Wang Y; School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China. |
| Source: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 Jun 12; Vol. 26 (12). Date of Electronic Publication: 2026 Jun 12. |
| Publication Type: |
Journal Article |
| Language: |
English |
| Journal Info: |
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: Basel, Switzerland : MDPI, c2000- |
| MeSH Terms: |
Imaging, Three-Dimensional*/methods , Fixation, Ocular*/physiology , Image Processing, Computer-Assisted*/methods , Eye-Tracking Technology*, Eye Movements/physiology ; Humans ; Infrared Rays ; Algorithms |
| Abstract: |
Head-mounted eye-tracking systems play a critical role in virtual reality, human-computer interaction, and clinical applications, yet achieving both high angular accuracy and precise 3D gaze position estimation with low-cost hardware remains challenging. This paper proposes a lightweight, training-free geometric 3D gaze tracking framework for binocular 3D gaze tracking using consumer-grade hardware, which leverages stereo geometric triangulation and a simplified physiological eye model to achieve robust 3D gaze estimation, requiring only standard infrared cameras and dichroic mirrors without additional specialized hardware. The method was evaluated in controlled indoor conditions with 30 participants, where it achieved an angular error ranging from 1.1° to 2.82° and a 3D gaze position error below 13.24 mm. Compared to two state-of-the-art academic non-deep-learning methods, the proposed framework delivers competitive angular accuracy while significantly reducing 3D position error, outperforming the baselines by 34% to 56% in depth estimation precision. These results demonstrates that the proposed geometric framework is a practical and effective solution for high-precision 3D gaze tracking on low-cost hardware, suitable for both research and consumer applications. |
| Grant Information: |
62405131 National Natural Science Foundation of China (NSFC) Young Scientists Fund |
| Contributed Indexing: |
Keywords: 3D gaze tracking; eye tracking; geometric modeling; infrared image processing; lightweight framework |
| Entry Date(s): |
Date Created: 20260626 Date Completed: 20260626 Latest Revision: 20260726 |
| Update Code: |
20260726 |
| PubMed Central ID: |
PMC13306384 |
| DOI: |
10.3390/s26123741 |
| PMID: |
42356714 |
| Database: |
MEDLINE |