Effects of Different Datasets, Models, Face-parts on Accuracy and Performance of Intelligent Facial Expression Recognition Systems


  • Sharmeen M. Salim Abdullah Information Technology Dept., Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
  • Subhi R. M. Zeebaree Energy Eng. Dept., Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq,
  • Maiwan B. Abdulrazzaq Computer Science Dept., Faculty of Science, University of Zakho, Duhok, Iraq,


Facial behavior analysis, Facial expression recognition, Datasets, Tools, Models, Complete facial recognition, incomplete facial recognition


Facial expression recognition is a crucial area of study in the field of computer vision. Research on nonverbal communication has shown that a significant amount of deliberate information is sent via facial expressions. Facial expression recognition is a crucial field in computer vision that deals with the significant impact of nonverbal communication. Expression recognition has lately been extensively used in the medical and advertising sectors. Difficulties in Facial Emotion Recognition. Facial emotion recognition is a technique that examines facial expressions in static photos and videos to uncover information about an individual's emotional state. The intricacy of facial expressions, the versatile use of the technology in any setting, and the incorporation of emerging technologies like artificial intelligence pose substantial privacy hazards. Facial expressions serve as non-verbal cues, offering indications of human emotions. Deciphering emotional expressions has been a focal point of study in psychology for many years. This study will examine several prior studies that have undertaken comprehensive facial analysis, including both total and partial face recognition, to identify expressions and emotions. The datasets and models used in previous studies, as well as the findings gained, show that employing the whole face yields more accuracy compared to using specific face-parts, which result in lower accuracy ratios. However, emotional identification often does not rely only on the whole face, since it is not always feasible to have the full face available. Contemporary research is now prioritising the identification of facial expressions based on certain facial features. Efficient deep learning algorithms, particularly the CNN algorithm, can do this task.


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How to Cite

Abdullah, S. M. S. ., Zeebaree, S. R. M. ., & Abdulrazzaq, M. B. . (2024). Effects of Different Datasets, Models, Face-parts on Accuracy and Performance of Intelligent Facial Expression Recognition Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 366–381. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4759



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