Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications
In this paper, the development of an eye-tracking-based human−computer interface for real-time applications is presented. To identify the most appropriate pupil detection algorithm for the proposed interface, we analyzed the performance of eight algorithms, six of which we developed based...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/16/3630 |
| _version_ | 1828151854565949440 |
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| author | Radu Gabriel Bozomitu Alexandru Păsărică Daniela Tărniceriu Cristian Rotariu |
| author_facet | Radu Gabriel Bozomitu Alexandru Păsărică Daniela Tărniceriu Cristian Rotariu |
| author_sort | Radu Gabriel Bozomitu |
| collection | DOAJ |
| description | In this paper, the development of an eye-tracking-based human−computer interface for real-time applications is presented. To identify the most appropriate pupil detection algorithm for the proposed interface, we analyzed the performance of eight algorithms, six of which we developed based on the most representative pupil center detection techniques. The accuracy of each algorithm was evaluated for different eye images from four representative databases and for video eye images using a new testing protocol for a scene image. For all video recordings, we determined the detection rate within a circular target 50-pixel area placed in different positions in the scene image, cursor controllability and stability on the user screen, and running time. The experimental results for a set of 30 subjects show a detection rate over 84% at 50 pixels for all proposed algorithms, and the best result (91.39%) was obtained with the circular Hough transform approach. Finally, this algorithm was implemented in the proposed interface to develop an eye typing application based on a virtual keyboard. The mean typing speed of the subjects who tested the system was higher than 20 characters per minute. |
| first_indexed | 2024-04-11T22:03:39Z |
| format | Article |
| id | doaj.art-e34537612723486097efd8fb401151f0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:03:39Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e34537612723486097efd8fb401151f02022-12-22T04:00:49ZengMDPI AGSensors1424-82202019-08-011916363010.3390/s19163630s19163630Development of an Eye Tracking-Based Human-Computer Interface for Real-Time ApplicationsRadu Gabriel Bozomitu0Alexandru Păsărică1Daniela Tărniceriu2Cristian Rotariu3Faculty of Electronics, Telecommunications and Information Technology, “Gheorghe Asachi” Technical University, Iaşi 700050, RomaniaFaculty of Electronics, Telecommunications and Information Technology, “Gheorghe Asachi” Technical University, Iaşi 700050, RomaniaFaculty of Electronics, Telecommunications and Information Technology, “Gheorghe Asachi” Technical University, Iaşi 700050, RomaniaFaculty of Medical Bioengineering, “Grigore T. Popa” University of Medicine and Pharmacy, Iaşi 700115, RomaniaIn this paper, the development of an eye-tracking-based human−computer interface for real-time applications is presented. To identify the most appropriate pupil detection algorithm for the proposed interface, we analyzed the performance of eight algorithms, six of which we developed based on the most representative pupil center detection techniques. The accuracy of each algorithm was evaluated for different eye images from four representative databases and for video eye images using a new testing protocol for a scene image. For all video recordings, we determined the detection rate within a circular target 50-pixel area placed in different positions in the scene image, cursor controllability and stability on the user screen, and running time. The experimental results for a set of 30 subjects show a detection rate over 84% at 50 pixels for all proposed algorithms, and the best result (91.39%) was obtained with the circular Hough transform approach. Finally, this algorithm was implemented in the proposed interface to develop an eye typing application based on a virtual keyboard. The mean typing speed of the subjects who tested the system was higher than 20 characters per minute.https://www.mdpi.com/1424-8220/19/16/3630detection rateeye trackinghuman computer interactionimage processingopen source softwarepupil detection algorithms |
| spellingShingle | Radu Gabriel Bozomitu Alexandru Păsărică Daniela Tărniceriu Cristian Rotariu Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications Sensors detection rate eye tracking human computer interaction image processing open source software pupil detection algorithms |
| title | Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications |
| title_full | Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications |
| title_fullStr | Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications |
| title_full_unstemmed | Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications |
| title_short | Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications |
| title_sort | development of an eye tracking based human computer interface for real time applications |
| topic | detection rate eye tracking human computer interaction image processing open source software pupil detection algorithms |
| url | https://www.mdpi.com/1424-8220/19/16/3630 |
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