Effective Facial Expression Recognition System Using Artificial Intelligence Technique

Facial expressions are the most basic non-verbal method people use to communicate feelings, intentions and reactions without words. Recognizing these facial expressions accurately is essential for a variety of applications — such as tools that use our faces to interact with computers (human-compute...

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Main Authors: Imad S. Yousif, Tarik A. Rashid, Ahmed S. Shamsaldin, Sabat A. Abdulhameed, Abdulhady Abas Abdullah
Format: Article
Language:English
Published: Sulaimani Polytechnic University 2024-12-01
Series:Kurdistan Journal of Applied Research
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Online Access:https://kjar.spu.edu.iq/index.php/kjar/article/view/974
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author Imad S. Yousif
Tarik A. Rashid
Ahmed S. Shamsaldin
Sabat A. Abdulhameed
Abdulhady Abas Abdullah
author_facet Imad S. Yousif
Tarik A. Rashid
Ahmed S. Shamsaldin
Sabat A. Abdulhameed
Abdulhady Abas Abdullah
author_sort Imad S. Yousif
collection DOAJ
description Facial expressions are the most basic non-verbal method people use to communicate feelings, intentions and reactions without words. Recognizing these facial expressions accurately is essential for a variety of applications — such as tools that use our faces to interact with computers (human-computer interaction, or HCI), security systems and emotionally intelligent artificial intelligence technologies. As the complexities surrounding these relationships have become better understood, it has allowed us to develop increasingly more complex systems for identifying and detecting facial expressions of different emotions. This paper presents an improved performance of the Facial Expression Recognition (FER) systems via augmentation in Artificial Neural Networks and Genetic Algorithms, two renowned artificial intelligence techniques possessing disparate strengths. ANNS are inspired by the neural architecture of human brain capable of learning and recognizing patterns in unchartered data after trained examples, on the other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, and mutation. The research is an effort to mitigate the problems pertaining with conventional methods, like overfitting and generalization fault in order design FER model which has potential for performing much more accurately. A hybrid ANN-GA model that uses Petri Nets and production systems is proposed for the real-time video sequence analysis with high precision in predicting different dynamic facial activities of anger, surprise, disgust, joy, sadness and fear from emotion faces. Importantly, results on the study show that this integrated model has a large-scale promoting effect in emotion detection upon varied scenes and is therefore generalizable to many domains from security and surveillance over biomedicine up to interactive AI-driven systems. Implications for implementing real-time and context-aware recognition of human emotions based on AI technologies are far-reaching as they demonstrate the potential that hybrid AI systems offer at enhancing emotion deciphering.
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institution Kabale University
issn 2411-7684
2411-7706
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publishDate 2024-12-01
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spelling doaj-art-36f1915f693345868d663ad42b96a0c12025-02-09T20:59:27ZengSulaimani Polytechnic UniversityKurdistan Journal of Applied Research2411-76842411-77062024-12-019210.24017/science.2024.2.9Effective Facial Expression Recognition System Using Artificial Intelligence TechniqueImad S. Yousif0https://orcid.org/0009-0008-7511-0059Tarik A. Rashid1https://orcid.org/0000-0002-8661-258XAhmed S. Shamsaldin2https://orcid.org/0000-0002-4148-0333Sabat A. Abdulhameed3https://orcid.org/0009-0000-3861-492XAbdulhady Abas Abdullah4https://orcid.org/0009-0007-5508-9371Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq.Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq | Artificial Intelligence and Innovation Center, University of Kurdistan Hewler, Erbil, IraqComputer Science and Engineering Department, University of Kurdistan Hewler, Erbil, IraqComputer Science and Engineering Department, University of Kurdistan Hewler, Erbil, IraqArtificial Intelligence and Innovation Center, University of Kurdistan Hewler, Erbil, Iraq Facial expressions are the most basic non-verbal method people use to communicate feelings, intentions and reactions without words. Recognizing these facial expressions accurately is essential for a variety of applications — such as tools that use our faces to interact with computers (human-computer interaction, or HCI), security systems and emotionally intelligent artificial intelligence technologies. As the complexities surrounding these relationships have become better understood, it has allowed us to develop increasingly more complex systems for identifying and detecting facial expressions of different emotions. This paper presents an improved performance of the Facial Expression Recognition (FER) systems via augmentation in Artificial Neural Networks and Genetic Algorithms, two renowned artificial intelligence techniques possessing disparate strengths. ANNS are inspired by the neural architecture of human brain capable of learning and recognizing patterns in unchartered data after trained examples, on the other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, and mutation. The research is an effort to mitigate the problems pertaining with conventional methods, like overfitting and generalization fault in order design FER model which has potential for performing much more accurately. A hybrid ANN-GA model that uses Petri Nets and production systems is proposed for the real-time video sequence analysis with high precision in predicting different dynamic facial activities of anger, surprise, disgust, joy, sadness and fear from emotion faces. Importantly, results on the study show that this integrated model has a large-scale promoting effect in emotion detection upon varied scenes and is therefore generalizable to many domains from security and surveillance over biomedicine up to interactive AI-driven systems. Implications for implementing real-time and context-aware recognition of human emotions based on AI technologies are far-reaching as they demonstrate the potential that hybrid AI systems offer at enhancing emotion deciphering. https://kjar.spu.edu.iq/index.php/kjar/article/view/974Facial Expression RecognitionArtificial Neural NetworksGenetic Algorithms
spellingShingle Imad S. Yousif
Tarik A. Rashid
Ahmed S. Shamsaldin
Sabat A. Abdulhameed
Abdulhady Abas Abdullah
Effective Facial Expression Recognition System Using Artificial Intelligence Technique
Kurdistan Journal of Applied Research
Facial Expression Recognition
Artificial Neural Networks
Genetic Algorithms
title Effective Facial Expression Recognition System Using Artificial Intelligence Technique
title_full Effective Facial Expression Recognition System Using Artificial Intelligence Technique
title_fullStr Effective Facial Expression Recognition System Using Artificial Intelligence Technique
title_full_unstemmed Effective Facial Expression Recognition System Using Artificial Intelligence Technique
title_short Effective Facial Expression Recognition System Using Artificial Intelligence Technique
title_sort effective facial expression recognition system using artificial intelligence technique
topic Facial Expression Recognition
Artificial Neural Networks
Genetic Algorithms
url https://kjar.spu.edu.iq/index.php/kjar/article/view/974
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