Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning
This study used machine learning algorithms to identify actors and extract the age of actors from images taken randomly from movies. The use of images taken from Arab movies includes challenges such as non-uniform lighting, different and multiple poses for the actors and multiple elements with the...
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University of Baghdad, College of Science for Women
2024-12-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8996 |
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| author | Asraa Muayed Abdalah Noor Redha Alkazaz |
| author_facet | Asraa Muayed Abdalah Noor Redha Alkazaz |
| author_sort | Asraa Muayed Abdalah |
| collection | DOAJ |
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This study used machine learning algorithms to identify actors and extract the age of actors from images taken randomly from movies. The use of images taken from Arab movies includes challenges such as non-uniform lighting, different and multiple poses for the actors and multiple elements with the actor or a group of actors. Additionally, the use of make-up, wigs, beards, and wearing different accessories and costumes made it difficult for the system to identify the personality of the same actor. The Arab Actors Dataset-AAD comprises 574 images sourced from various movies, encompassing both black and white as well as color compositions. The images depict complete scenes or fragments thereof. Multiple models were employed for feature extraction, and diverse machine learning algorithms were utilized during the classification and prediction stages to determine the most effective algorithm for handling such image types. The study demonstrated the effectiveness of the Logistic Regression model exhibited the best performance compared to other models in the training phase, as evidenced by its AUC, precision, CA and F1score values of 99%, 86%, 85.5% and 84.2% respectively. The findings of this study can be used to improve the precision and reliability of facial recognition technology for various uses as with movies search services, movie suggestion algorithms, and genre classification of movies.
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| format | Article |
| id | doaj-art-27d7543ba1214d40a46705d7646b0a99 |
| institution | DOAJ |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-27d7543ba1214d40a46705d7646b0a992025-08-20T03:14:36ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862024-12-012112(Suppl.)10.21123/bsj.2024.8996Predicting Movie Production Years through Facial Recognition of Actors with Machine LearningAsraa Muayed Abdalah0https://orcid.org/0009-0009-9335-9779Noor Redha Alkazaz1https://orcid.org/0000-0002-5934-3767Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq.Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq. This study used machine learning algorithms to identify actors and extract the age of actors from images taken randomly from movies. The use of images taken from Arab movies includes challenges such as non-uniform lighting, different and multiple poses for the actors and multiple elements with the actor or a group of actors. Additionally, the use of make-up, wigs, beards, and wearing different accessories and costumes made it difficult for the system to identify the personality of the same actor. The Arab Actors Dataset-AAD comprises 574 images sourced from various movies, encompassing both black and white as well as color compositions. The images depict complete scenes or fragments thereof. Multiple models were employed for feature extraction, and diverse machine learning algorithms were utilized during the classification and prediction stages to determine the most effective algorithm for handling such image types. The study demonstrated the effectiveness of the Logistic Regression model exhibited the best performance compared to other models in the training phase, as evidenced by its AUC, precision, CA and F1score values of 99%, 86%, 85.5% and 84.2% respectively. The findings of this study can be used to improve the precision and reliability of facial recognition technology for various uses as with movies search services, movie suggestion algorithms, and genre classification of movies. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8996Artificial Intelligence, Machine Learning Algorithms, Face Recognition, Age Prediction, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN) |
| spellingShingle | Asraa Muayed Abdalah Noor Redha Alkazaz Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning مجلة بغداد للعلوم Artificial Intelligence, Machine Learning Algorithms, Face Recognition, Age Prediction, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN) |
| title | Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning |
| title_full | Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning |
| title_fullStr | Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning |
| title_full_unstemmed | Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning |
| title_short | Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning |
| title_sort | predicting movie production years through facial recognition of actors with machine learning |
| topic | Artificial Intelligence, Machine Learning Algorithms, Face Recognition, Age Prediction, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN) |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8996 |
| work_keys_str_mv | AT asraamuayedabdalah predictingmovieproductionyearsthroughfacialrecognitionofactorswithmachinelearning AT noorredhaalkazaz predictingmovieproductionyearsthroughfacialrecognitionofactorswithmachinelearning |