Best low-cost methods for real-time detection of the eye and gaze tracking
| dc.contributor.author | Khaleel, Amal Hameed | |
| dc.contributor.author | Abbas, Thekra H. | |
| dc.contributor.author | Ibrahim, Abdul-Wahab Sami | |
| dc.date.accessioned | 2024-04-30T04:47:25Z | |
| dc.date.available | 2024-04-30T04:47:25Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The study of gaze tracking is a significant research area in computer vision. It focuses on real-world applications and the interface between humans and computers. Recently, new eye-tracking applications have boosted the need for low-cost methods. The eye region is a crucial aspect of tracking the direction of the gaze. In this paper, several new methods have been proposed for eye-tracking by using methods to determine the eye area as well as find the direction of gaze. Unmodified webcams can be used for eye-tracking without the need for specialized equipment or software. Two methods for determining the eye region were used: facial landmarks or the Haar cascade technique. Moreover, the direct method, based on the convolutional neural network model, and the engineering method, based on distances determining the iris region, were used to determine the eye’s direction. The paper uses two engineering techniques: drawing perpendicular lines on the iris region to identify the gaze direction junction point and dividing the eye region into five regions, with the blackest region representing the gaze direction. The proposed network model has proven effective in determining the eye’s gaze direction within limited mobility, while engineering methods improve their effectiveness in wide mobility. | en |
| dc.identifier.doi | 10.1515/icom-2023-0026 | |
| dc.identifier.issn | 2196-6826 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43990 | |
| dc.language.iso | en | |
| dc.pubPlace | Berlin | |
| dc.publisher | De Gruyter | |
| dc.relation.ispartof | i-com: Vol. 23, No. 1 | |
| dc.subject | computer vision | |
| dc.subject | human computer interaction | |
| dc.subject | deep learning | |
| dc.subject | eye gaze tracking | |
| dc.subject | iris landmarks | |
| dc.title | Best low-cost methods for real-time detection of the eye and gaze tracking | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 94 | |
| gi.citation.startPage | 79 | |
| gi.conference.sessiontitle | Research Article |