phat 23(1):

Research Article

Algorithms used for facial emotion recognition: a systematic review of the literature

Download40 downloads
  • @ARTICLE{10.4108/eetpht.9.4214,
        author={Jos\^{e} Armando Tiznado Ubill\^{u}s and Jos\^{e} Alfredo Herrera Quispe and Luis Antonio Rivera Escriba and Marysela Ladera-Casta\`{o}eda and C\^{e}sar Augusto Atoche Pacherres and Miguel \^{A}ngel Atoche Pacherres and Carmen Lucila Infante Saavedra},
        title={Algorithms used for facial emotion recognition: a systematic review of the literature},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={Facial emotion, computer vision, Deep Learning, Machine Learning, Algorithm},
  • José Armando Tiznado Ubillús
    José Alfredo Herrera Quispe
    Luis Antonio Rivera Escriba
    Marysela Ladera-Castañeda
    César Augusto Atoche Pacherres
    Miguel Ángel Atoche Pacherres
    Carmen Lucila Infante Saavedra
    Year: 2023
    Algorithms used for facial emotion recognition: a systematic review of the literature
    DOI: 10.4108/eetpht.9.4214
José Armando Tiznado Ubillús1,*, José Alfredo Herrera Quispe1, Luis Antonio Rivera Escriba2, Marysela Ladera-Castañeda3, César Augusto Atoche Pacherres4, Miguel Ángel Atoche Pacherres5, Carmen Lucila Infante Saavedra4
  • 1: National University of San Marcos
  • 2: State University of Norte Fluminense
  • 3: Federico Villarreal National University
  • 4: Universidad Nacional de Piura
  • 5: Universidad César Vallejo
*Contact email:


INTRODUCTION: We currently live in a society that is constantly changing and technology has developed algorithms that allow facial emotion recognition, because facial expression transmits people's mood, feelings and state of soul. However, it is required that future research can improve the quality of emotion detection by improving the quality of the data set and the model used, for this reason, it is necessary to investigate other machine learning algorithms in the recognition of facial emotions, as they exist. identification deficiencies that limit the discrimination of extracted structural features. OBJECTIVE: The purpose of the article was to analyze the most used algorithms for facial emotion recognition, through a systematic literature review, according to the PRISMA method. METHOD: A search for information was carried out in articles published in open access such as: Scopus, Web of Science (WOS) and Association for Computing Machiner (ACM) in the period 2022 and 2023, totaling 38 selected articles. RESULTS: The results obtained indicate that the algorithms most used by the authors are SVM and SoftMax with a total of 17.65% each. CONCLUSION: It is concluded that the SVM and SoftMax algorithms are the most predominant, playing a crucial role in achieving optimal levels of precision in the training of the models. These algorithms, with their robustness and ability to deal with complex data, have proven to be fundamental pillars in the field of facial emotion recognition.