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Authors: Pratikshya Sharma 1 ; Sonya Coleman 1 ; Pratheepan Yogarajah 1 ; Laurence Taggart 2 and Pradeepa Samarasinghe 3

Affiliations: 1 School of Computing, Engineering & Intelligent Systems, Ulster University, Northern Ireland, U.K. ; 2 School of Nursing & Health Research, Ulster University, Northern Ireland, U.K. ; 3 Department of Information Technology, Malabe, Sri Lanka

Keyword(s): Micro Expression, General Adversarial Network, Super Resolution.

Abstract: The Advancements in micro expression recognition techniques are accelerating at an exceptional rate in recent years. Envisaging a real environment, the recordings captured in our everyday life are prime sources for many studies, but these data often suffer from poor quality. Consequently, this has opened up a new research direction involving low resolution micro expression images. Identifying a particular class of micro expression among several classes is extremely challenging due to less distinct inter-class discriminative features. Low resolution of such images further diminishes the discriminative power of micro facial features. Undoubtedly, this increases the recognition challenge by twofold. To address the issue of low-resolution for facial micro expression, this work proposes a novel approach that employs a super resolution technique using Generative Adversarial Network and its variant. Additionally, Local Binary Pattern & Local phase quantization on three orthogonal planes are used for extracting facial micro features. The overall performance is evaluated based on recognition accuracy obtained using a support vector machine. Also, image quality metrics are used for evaluating reconstruction performance. Low resolution images simulated from the SMIC-HS dataset are used for testing the proposed approach and experimental results demonstrate its usefulness. (More)

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Paper citation in several formats:
Sharma, P.; Coleman, S.; Yogarajah, P.; Taggart, L. and Samarasinghe, P. (2022). Evaluation of Generative Adversarial Network Generated Super Resolution Images for Micro Expression Recognition. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-758-549-4; ISSN 2184-4313, pages 560-569. DOI: 10.5220/0010820100003122

@conference{icpram22,
author={Pratikshya Sharma. and Sonya Coleman. and Pratheepan Yogarajah. and Laurence Taggart. and Pradeepa Samarasinghe.},
title={Evaluation of Generative Adversarial Network Generated Super Resolution Images for Micro Expression Recognition},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,},
year={2022},
pages={560-569},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010820100003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Evaluation of Generative Adversarial Network Generated Super Resolution Images for Micro Expression Recognition
SN - 978-989-758-549-4
IS - 2184-4313
AU - Sharma, P.
AU - Coleman, S.
AU - Yogarajah, P.
AU - Taggart, L.
AU - Samarasinghe, P.
PY - 2022
SP - 560
EP - 569
DO - 10.5220/0010820100003122