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Authors: Rohan Kumar Gupta and Dakshina Ranjan Kisku

Affiliation: Department of Computer Science and Engineering, National Institute of Technology Durgapur, West Bengal, India

Keyword(s): Perception Analysis, Multi-view Learning, Biometrics.

Abstract: The visual facial features do reveal a lot about an individual and can be used to analyse several important social attributes. Existing works have shown that it is possible to learn these attributes through computational models and classify or score subject-faces accordingly. However, we find that there exists local variance in perception. There could be different perspectives of the face which the conventional methods fail to efficiently capture. We also note that Deeper neural networks usually require enough training data and add little to no improvement upon existing ones. In this work, we take social attribute prediction a notch higher and propose a novel multi-view regression approach to incorporate multiple views of face inspired by multi-modal learning. Experimental results show that the proposed approach can achieve superior feature generalisation and diversification on existing datasets using multiple views to improve the coefficient of determination scores and outperforms t he state-of-the-art social attribute prediction method. We further propose a method that enables real-time video analysis of multiple subject faces which can have several applications. (More)

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Paper citation in several formats:
Gupta, R. and Kisku, D. (2022). ImpressionNet: A Multi-view Approach to Predict Socio-facial Impressions. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 33-38. DOI: 10.5220/0010762400003116

@conference{icaart22,
author={Rohan Kumar Gupta and Dakshina Ranjan Kisku},
title={ImpressionNet: A Multi-view Approach to Predict Socio-facial Impressions},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={33-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010762400003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - ImpressionNet: A Multi-view Approach to Predict Socio-facial Impressions
SN - 978-989-758-547-0
IS - 2184-433X
AU - Gupta, R.
AU - Kisku, D.
PY - 2022
SP - 33
EP - 38
DO - 10.5220/0010762400003116
PB - SciTePress

- Science and Technology Publications, Lda.
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