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Facial landmark detection and geometric feature-based emotion recognition

Facial landmark detection and geometric feature-based emotion recognition Facial expression related to machine intelligence is a popular research area in emotion science, pain assessment, human behaviour analysis, virtual reality, etc. This paper aims at exploring a contour-based shape analysis from the viewpoint of geometric characteristics towards facial expression recognition. Since the facial landmark detection accuracy dramatically affects the final classification, a simple contour detection algorithm is used for identifying facial landmarks accurately. Spatial local and relative geometric features extracted with the neutral face as the reference are projected to the lower-dimensional space using stepwise linear discriminant analysis. The proposed system is tested and validated using backpropagation-based artificial neural network on JAFFE and MMI dataset with an average accuracy of 95.53% and 94.98%, respectively. The proposed scheme's recognition accuracy has been compared with the state-of-art methods, and the results show significant improvement in the proposed model over others using geometric features alone. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Facial landmark detection and geometric feature-based emotion recognition

International Journal of Biometrics , Volume 14 (2): 17 – Jan 1, 2022

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/ijbm.2022.121799
Publisher site
See Article on Publisher Site

Abstract

Facial expression related to machine intelligence is a popular research area in emotion science, pain assessment, human behaviour analysis, virtual reality, etc. This paper aims at exploring a contour-based shape analysis from the viewpoint of geometric characteristics towards facial expression recognition. Since the facial landmark detection accuracy dramatically affects the final classification, a simple contour detection algorithm is used for identifying facial landmarks accurately. Spatial local and relative geometric features extracted with the neutral face as the reference are projected to the lower-dimensional space using stepwise linear discriminant analysis. The proposed system is tested and validated using backpropagation-based artificial neural network on JAFFE and MMI dataset with an average accuracy of 95.53% and 94.98%, respectively. The proposed scheme's recognition accuracy has been compared with the state-of-art methods, and the results show significant improvement in the proposed model over others using geometric features alone.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2022

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