PARKS-Gaze - A Precision-focused Gaze Estimation Dataset in the Wild under Extreme Head Poses

Murthy, LRD and Mukhopadhyay, A and Anand, K and Aggarwal, S and Biswas, P (2022) PARKS-Gaze - A Precision-focused Gaze Estimation Dataset in the Wild under Extreme Head Poses. In: 27th International Conference on Intelligent User Interfaces, IUI 2022, 22 March 2022, Virtual, Online, pp. 81-84.

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Abstract

The performance of appearance-based gaze estimation systems that utilizes machine learning depends on training datasets. Most of the existing gaze estimation datasets were recorded in laboratory conditions. The datasets recorded in the wild conditions display limited head pose and intra-person variation. We proposed PARKS-Gaze, a gaze estimation dataset with 570 minutes of video data from 18 participants. We captured head pose range of ± 50, -40,60 degrees in yaw and pitch directions respectively. We captured multiple images for a single Point of Gaze (PoG) enabling to carry out precision analysis of gaze estimation models. Our cross-dataset experiments revealed that the model trained on proposed dataset obtained lower mean test errors than existing datasets, indicating its utility for developing real-world interactive gaze controlled applications. © 2022 Owner/Author.

Item Type:	Conference Proceedings
Publication:	International Conference on Intelligent User Interfaces, Proceedings IUI
Publisher:	Association for Computing Machinery
Additional Information:	The copyright for this article belongs to Association for Computing Machinery
Keywords:	Appearance based; Estimation models; Estimation systems; Gaze estimation; Gaze estimation dataset; Gaze estimation model; Head pose; Performance; Training dataset, Statistical tests
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	17 May 2022 10:47
Last Modified:	17 May 2022 10:47
URI:	https://eprints.iisc.ac.in/id/eprint/71781

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