research-article

Experimental Analysis of the Facial Expression Recognition of Male and Female

Authors:

Guangming Huang,

Kok-Hoe WongAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 26, Pages 1 - 5

https://doi.org/10.1145/3331453.3361634

Published: 22 October 2019 Publication History

Abstract

With the development of deep learning, people have paid more and more attention to the research of facial expression recognition (FER), and obtained decent results in the laboratory. However, some studies have pointed out the defects of FER system itself based on the universal theory of expression and believed that human expression is specific. The purpose of this study is to analyze the influence of different gender data on the recognition rate of FER classification system. This study needs to prove that the recognition rate of different gender data in the existing FER system is different. In addition, it is necessary to confirm that there is a population recognition advantage between different gender groups in the experiment. Experiments construct a classification system by Inception V3 and transfer learning methods and design a comparative experiment. It was found that data sets with different gender ratios did influence the experimental results to some extent, and the recognition rate of female data was slightly higher than that of male data. Finally, it is concluded that models trained by male data have a higher rate of expression recognition for male group, as is the case with female data, which is similar to the situation of different cultural groups.

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Cited By

Alfred RPailus RObit JLim YSukirno H(2023)A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Recognition to Enhance Learning ExperienceComputational Science – ICCS 202310.1007/978-3-031-36027-5_11(140-154)Online publication date: 26-Jun-2023
https://doi.org/10.1007/978-3-031-36027-5_11

Index Terms

Experimental Analysis of the Facial Expression Recognition of Male and Female
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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cover image ACM Other conferences

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2019

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Cited By

Alfred RPailus RObit JLim YSukirno H(2023)A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Recognition to Enhance Learning ExperienceComputational Science – ICCS 202310.1007/978-3-031-36027-5_11(140-154)Online publication date: 26-Jun-2023
https://doi.org/10.1007/978-3-031-36027-5_11

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