PDIF: Pupil Detection After Isolation and Fitting

Manuri, Federico; Sanna, Andrea; Petrucci, Christian Pio

doi:10.1109/ACCESS.2020.2973005

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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.

Titolo:	PDIF: Pupil Detection After Isolation and Fitting
Autori:	MANURI, FEDERICO SANNA, ANDREA Petrucci, Christian Pio mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2020
Rivista:	IEEE ACCESS
Digital Object Identifier (DOI):	10.1109/ACCESS.2020.2973005
Appare nelle tipologie:	1.1 Articolo in rivista

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Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/11583/2797356

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