International Journal of Computational Intelligence Systems

Volume 12, Issue 2, 2019, Pages 903 - 913

Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm

Authors
S. D. Lalitha1, *, K. K. Thyagharajan2
1 Assistant Professor, R.M.K Engineering College, Chennai, India
2 Professor & Dean (Academic), R.M.D Engineering College, Chennai, India
*Corresponding author. Email: sdl.cse@rmkec.ac.in
Corresponding Author
S. D. Lalitha
Received 24 April 2019, Accepted 19 July 2019, Available Online 14 August 2019.
DOI
https://doi.org/10.2991/ijcis.d.190801.001How to use a DOI?
Keywords
Adaptive homomorphic filtering, Micro-facial expression, Micro-facial expression based deep-rooted learning, center loss function, CNN, MAE
Abstract

Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this work, three major steps are involved to improve the performance of micro-facial expression recognition. First, an Adaptive Homomorphic Filtering is used for face detection and rotation rectification processes. Secondly, Micro-facial features were used to extract the appearance variations of a testing image-spatial analysis. The features of motion information are used for expression recognition in a sequence of facial images. An effective Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL) classifier is proposed in this paper to better recognize spontaneous micro-expressions by learning parameters on the optimal features. This proposed method includes two loss functions such as cross entropy loss function and center loss function. Then the performance of the algorithm will be evaluated using recognition rate and false measures. Simulation results show that the predictive performance of the proposed method outperforms that of the existing classifiers such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) in terms of accuracy and Mean Absolute Error (MAE).

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
903 - 913
Publication Date
2019/08
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.190801.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - S. D. Lalitha
AU  - K. K. Thyagharajan
PY  - 2019
DA  - 2019/08
TI  - Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 903
EP  - 913
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190801.001
DO  - https://doi.org/10.2991/ijcis.d.190801.001
ID  - Lalitha2019
ER  -