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Appearance-based gaze estimation using attention and difference mechanism

Murthy, LRD and Biswas, P (2021) Appearance-based gaze estimation using attention and difference mechanism. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops,, 19-25 jun 2021, pp. 3137-3146.

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Official URL: https://doi.org/10.1109/CVPRW53098.2021.00351


Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention-mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09 and 7.44 error on MPI-IGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset. © 2021 IEEE.

Item Type: Conference Paper
Publication: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society
Keywords: Computer vision; Deep learning, Appearance based; Appearance-based methods; Attention mechanisms; Common features; Estimation problem; Gaze estimation; Large datasets; Learning techniques; Model based approach; Novel techniques, Large dataset
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 07 Dec 2021 10:23
Last Modified: 07 Dec 2021 10:23
URI: http://eprints.iisc.ac.in/id/eprint/70385

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