PDIF: Pupil Detection After Isolation and Fitting
Pupil detection plays a key role in eye and gaze video-based tracking algorithms. Various algorithms have been proposed through the years in order to improve the performances or the robustness in real-world scenarios. However, the development of an algorithm which excels in both execution time and p...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8990093/ |
| _version_ | 1818379540083245056 |
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| author | Federico Manuri Andrea Sanna Christian Pio Petrucci |
| author_facet | Federico Manuri Andrea Sanna Christian Pio Petrucci |
| author_sort | Federico Manuri |
| collection | DOAJ |
| description | Pupil detection plays a key role in eye and gaze video-based tracking algorithms. Various algorithms have been proposed through the years in order to improve the performances or the robustness in real-world scenarios. However, the development of an algorithm which excels in both execution time and pupil detection precision is still an open challenge. This paper presents a novel, feature-based eye-tracking algorithm for pupil detection. Morphological operators are used to remove corneal reflections and to reduce noise in the pupil area prior to the pupil detection step: this solution allows to significantly reduce the computational overhead without lowering the tracking precision. Moreover, a shape validation step is performed after the elliptical fitting and, if the elliptical shape is not detected properly, a set of additional steps is performed to improve the pupil estimation. The proposed solution, Pupil Detection after Isolation and Fitting (PDIF), has been compared with other state-of-the-art tracking algorithms that use morphological operations such as ElSe (Ellipse Selection) and ExCuSe (Exclusive Curve Selector) to evaluate both speed and robustness; the proposed algorithm has been tested over numerous datasets offering different pupil detection challenges. Obtained results show how PDIF provides comparable tracking precision at a significantly lower computational cost compared to ElSe and ExCuSe. |
| first_indexed | 2024-12-14T02:04:24Z |
| format | Article |
| id | doaj.art-f3dba2690f084aacb5588f10cd405229 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:04:24Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f3dba2690f084aacb5588f10cd4052292022-12-21T23:20:56ZengIEEEIEEE Access2169-35362020-01-018308263083710.1109/ACCESS.2020.29730058990093PDIF: Pupil Detection After Isolation and FittingFederico Manuri0https://orcid.org/0000-0002-6599-9949Andrea Sanna1https://orcid.org/0000-0001-7916-1699Christian Pio Petrucci2https://orcid.org/0000-0001-6529-6971Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Turin, ItalyPay Reply, Turin, ItalyPupil detection plays a key role in eye and gaze video-based tracking algorithms. Various algorithms have been proposed through the years in order to improve the performances or the robustness in real-world scenarios. However, the development of an algorithm which excels in both execution time and pupil detection precision is still an open challenge. This paper presents a novel, feature-based eye-tracking algorithm for pupil detection. Morphological operators are used to remove corneal reflections and to reduce noise in the pupil area prior to the pupil detection step: this solution allows to significantly reduce the computational overhead without lowering the tracking precision. Moreover, a shape validation step is performed after the elliptical fitting and, if the elliptical shape is not detected properly, a set of additional steps is performed to improve the pupil estimation. The proposed solution, Pupil Detection after Isolation and Fitting (PDIF), has been compared with other state-of-the-art tracking algorithms that use morphological operations such as ElSe (Ellipse Selection) and ExCuSe (Exclusive Curve Selector) to evaluate both speed and robustness; the proposed algorithm has been tested over numerous datasets offering different pupil detection challenges. Obtained results show how PDIF provides comparable tracking precision at a significantly lower computational cost compared to ElSe and ExCuSe.https://ieeexplore.ieee.org/document/8990093/Pupil detectioneye detectioneye trackinggaze trackingimage processingimage analysis |
| spellingShingle | Federico Manuri Andrea Sanna Christian Pio Petrucci PDIF: Pupil Detection After Isolation and Fitting IEEE Access Pupil detection eye detection eye tracking gaze tracking image processing image analysis |
| title | PDIF: Pupil Detection After Isolation and Fitting |
| title_full | PDIF: Pupil Detection After Isolation and Fitting |
| title_fullStr | PDIF: Pupil Detection After Isolation and Fitting |
| title_full_unstemmed | PDIF: Pupil Detection After Isolation and Fitting |
| title_short | PDIF: Pupil Detection After Isolation and Fitting |
| title_sort | pdif pupil detection after isolation and fitting |
| topic | Pupil detection eye detection eye tracking gaze tracking image processing image analysis |
| url | https://ieeexplore.ieee.org/document/8990093/ |
| work_keys_str_mv | AT federicomanuri pdifpupildetectionafterisolationandfitting AT andreasanna pdifpupildetectionafterisolationandfitting AT christianpiopetrucci pdifpupildetectionafterisolationandfitting |