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Recognizing hidden emotions from difference image using mean local mapped pattern

Recent progress in computer vision has pushed the limit of facial recognition from human identification to micro-expressions (MEs). However, the visual analysis of MEs is still a very challenging task because of the short occurrence and insignificant intensity of the underlying signals. To date, the...

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Main Authors: Goh, Kam Meng, Sheikh, Usman Ullah, Maul, Tomás H.
Format: Article
Published: Springer New York LLC 2019
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/89015/
http://dx.doi.org/10.1007/s11042-019-7385-y
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spelling my.utm.890152021-01-26T08:39:09Z http://eprints.utm.my/id/eprint/89015/ Recognizing hidden emotions from difference image using mean local mapped pattern Goh, Kam Meng Sheikh, Usman Ullah Maul, Tomás H. TK Electrical engineering. Electronics Nuclear engineering Recent progress in computer vision has pushed the limit of facial recognition from human identification to micro-expressions (MEs). However, the visual analysis of MEs is still a very challenging task because of the short occurrence and insignificant intensity of the underlying signals. To date, the accuracy of recognizing hidden emotions from frames using conventional methods is still far from reaching saturation. To address this, we have proposed a new ME recognition approach based on Mean Local Mapped Pattern (M-LMP) as a texture feature, which outperforms other state-of-the art features in terms of accuracy due to its capability of capturing small pixel transitions. Inspired by previous work, we applied M-LMP to the difference image computed from an onset frame and an apex frame, where the former represents the frame with neutral emotion and the latter consists of the frame with the largest ME intensity. The extracted local features were classified using support vector machine (SVM) and K nearest neighbourhood (KNN) classifiers. The validation of the proposed approach was performed on the CASME II and CAS(ME)2 datasets, and the results were compared with other similar state-of-the-art approaches. Comprehensive experiments were conducted using various parameters to show the robustness of our approach in the imbalanced and small dataset. Springer New York LLC 2019-08 Article PeerReviewed Goh, Kam Meng and Sheikh, Usman Ullah and Maul, Tomás H. (2019) Recognizing hidden emotions from difference image using mean local mapped pattern. Multimedia Tools and Applications, 78 (15). pp. 21485-21520. ISSN 1380-7501 http://dx.doi.org/10.1007/s11042-019-7385-y
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Goh, Kam Meng
Sheikh, Usman Ullah
Maul, Tomás H.
Recognizing hidden emotions from difference image using mean local mapped pattern
description Recent progress in computer vision has pushed the limit of facial recognition from human identification to micro-expressions (MEs). However, the visual analysis of MEs is still a very challenging task because of the short occurrence and insignificant intensity of the underlying signals. To date, the accuracy of recognizing hidden emotions from frames using conventional methods is still far from reaching saturation. To address this, we have proposed a new ME recognition approach based on Mean Local Mapped Pattern (M-LMP) as a texture feature, which outperforms other state-of-the art features in terms of accuracy due to its capability of capturing small pixel transitions. Inspired by previous work, we applied M-LMP to the difference image computed from an onset frame and an apex frame, where the former represents the frame with neutral emotion and the latter consists of the frame with the largest ME intensity. The extracted local features were classified using support vector machine (SVM) and K nearest neighbourhood (KNN) classifiers. The validation of the proposed approach was performed on the CASME II and CAS(ME)2 datasets, and the results were compared with other similar state-of-the-art approaches. Comprehensive experiments were conducted using various parameters to show the robustness of our approach in the imbalanced and small dataset.
format Article
author Goh, Kam Meng
Sheikh, Usman Ullah
Maul, Tomás H.
author_facet Goh, Kam Meng
Sheikh, Usman Ullah
Maul, Tomás H.
author_sort Goh, Kam Meng
title Recognizing hidden emotions from difference image using mean local mapped pattern
title_short Recognizing hidden emotions from difference image using mean local mapped pattern
title_full Recognizing hidden emotions from difference image using mean local mapped pattern
title_fullStr Recognizing hidden emotions from difference image using mean local mapped pattern
title_full_unstemmed Recognizing hidden emotions from difference image using mean local mapped pattern
title_sort recognizing hidden emotions from difference image using mean local mapped pattern
publisher Springer New York LLC
publishDate 2019
url http://eprints.utm.my/id/eprint/89015/
http://dx.doi.org/10.1007/s11042-019-7385-y
_version_ 1690370957432061952
score 13.211869

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