A Robust Eyeball Detection based on Computer Vision Approaches

Authors: R. Priyanka, S. Sridhar, R. Surya, J. Vimal James, P. Kavitha

Country: India

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Abstract: Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject’s looking direction is a challenging task, but eye contact can be effectively captured by a wearable point-of-view camera which provides a unique viewpoint. While moments of eye contact from this viewpoint can be hand-coded, such a process tends to be laborious and subjective. In this work, we develop a deep neural network model to automatically detect eye contact in egocentric video. It is the first to achieve accuracy equivalent to that of human experts. We train a deep convolutional network using a dataset of 4,339,879 annotated images, consisting of 103 subjects with diverse demographic backgrounds. The network achieves overall precision of 0.936 and recall of 0.943 on 18 validation subjects, and its performance is on par with 10 trained human coders with a mean precision 0.918 and recall 0.946. We used a media pipe package for iris movement such as left, right and center. And also, the eye close and open feature.

Keywords: Residual Network, Rectified Linear Unit, Exponential Linear Units, Artificial Neural Network

Paper Id: 230419

Published On: 2024-01-05

Published In: Volume 12, Issue 1, January-February 2024

Cite This: A Robust Eyeball Detection based on Computer Vision Approaches - R. Priyanka, S. Sridhar, R. Surya, J. Vimal James, P. Kavitha - IJIRMPS Volume 12, Issue 1, January-February 2024.

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