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research-article

Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm

Authors:

Ram SarkarAuthors Info & Claims

Volume 78, Issue 18

Pages 25753 - 25779

https://doi.org/10.1007/s11042-019-07811-x

Published: 01 September 2019 Publication History

Abstract

Facial Emotion Recognition (FER) is an important research domain which allows us to provide a better interactive environment between humans and computers. Some standard and popular features extracted from facial expression images include Uniform Local Binary Pattern (uLBP), Horizontal-Vertical Neighborhood Local Binary Pattern (hvnLBP), Gabor filters, Histogram of Oriented Gradients (HOG) and Pyramidal HOG (PHOG). However, these feature vectors may contain some features that are irrelevant or redundant in nature, thereby increasing the overall computational time as well as recognition error of a classification system. To counter this problem, we have proposed a new feature selection (FS) algorithm based on Late Hill Climbing and Memetic Algorithm (MA). A novel local search technique called Late Acceptance Hill Climbing through Redundancy and Relevancy (LAHCRR) has been used in this regard. It combines the concepts of Local Hill-Climbing and minimal-Redundancy Maximal-Relevance (mRMR) to form a more effective local search mechanism in MA. The algorithm is then evaluated on the said feature vectors extracted from the facial images of two popular FER datasets, namely RaFD and JAFFE. LAHCRR is used as local search in MA to form Late Hill Climbing based Memetic Algorithm (LHCMA). LHCMA is compared with state-of-the-art methods. The experimental outcomes show that the proposed FS algorithm reduces the feature dimension to a significant amount as well as increases the recognition accuracy as compared to other methods.

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Cited By

Ahmed SSheikh KMirjalili SSarkar R(2022)Binary Simulated Normal Distribution Optimizer for feature selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116834200:COnline publication date: 15-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116834
Vikas GGera DBalasubramanian S(2022)Instance Discrimination Based Robust Training for Facial Expression Recognition Under Noisy LabelsSN Computer Science10.1007/s42979-022-01410-84:1Online publication date: 15-Oct-2022
https://dl.acm.org/doi/10.1007/s42979-022-01410-8
Saurav SSaini RSingh S(2022)Fast facial expression recognition using Boosted Histogram of Oriented Gradient (BHOG) featuresPattern Analysis & Applications10.1007/s10044-022-01112-026:1(381-402)Online publication date: 20-Sep-2022
https://dl.acm.org/doi/10.1007/s10044-022-01112-0
Show More Cited By

Index Terms

Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
    2. Search methodologies
  2. Machine learning
2. Mathematics of computing
  1. Mathematical analysis

Index terms have been assigned to the content through auto-classification.

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 78, Issue 18

Sep 2019

1576 pages

ISSN:1380-7501

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Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

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Published: 01 September 2019

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Cited By

Ahmed SSheikh KMirjalili SSarkar R(2022)Binary Simulated Normal Distribution Optimizer for feature selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116834200:COnline publication date: 15-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116834
Vikas GGera DBalasubramanian S(2022)Instance Discrimination Based Robust Training for Facial Expression Recognition Under Noisy LabelsSN Computer Science10.1007/s42979-022-01410-84:1Online publication date: 15-Oct-2022
https://dl.acm.org/doi/10.1007/s42979-022-01410-8
Saurav SSaini RSingh S(2022)Fast facial expression recognition using Boosted Histogram of Oriented Gradient (BHOG) featuresPattern Analysis & Applications10.1007/s10044-022-01112-026:1(381-402)Online publication date: 20-Sep-2022
https://dl.acm.org/doi/10.1007/s10044-022-01112-0
Anjani Suputri Devi D Satyanarayana Ch (2021)An efficient facial emotion recognition system using novel deep learning neural network-regression activation classifierMultimedia Tools and Applications10.1007/s11042-021-10547-280:12(17543-17568)Online publication date: 8-Feb-2021
https://dl.acm.org/doi/10.1007/s11042-021-10547-2
Guha RKhan ASingh PSarkar RBhattacharjee D(2021)CGA: a new feature selection model for visual human action recognitionNeural Computing and Applications10.1007/s00521-020-05297-533:10(5267-5286)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1007/s00521-020-05297-5
Guha RGhosh MMutsuddi SSarkar RMirjalili S(2020)Embedded chaotic whale survival algorithm for filter–wrapper feature selectionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05183-124:17(12821-12843)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s00500-020-05183-1

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