Research Article
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Analysis of Emotional Authenticity Displayed by Film Actors Using Image Processing Techniques

Year 2024, Volume: 14 Issue: 2, 122 - 127

Abstract

This study analyzes a social phenomenon using technical methods to uncover the underlying reasons for the researched phenomenon. This study investigates the authenticity of facial expressions and emotional cues of well-known film actors in Turkish comedy cinema films. For the study, 480 video data samples related to the actors were collected from the social media platform YouTube. The videos were categorized into smile, surprise, and anger, with 120 samples analyzed for each category. The Kaggle database containing facial expressions of smile, surprise, and anger from regular individuals was utilized to compare the images.
The Local Binary Pattern (LBP) feature extraction technique was employed to extract features from the images. Machine learning models were then constructed using the extracted features. Based on the classification results, the accuracy values were 99.37% for the smile category, 97.19% for the surprise category, and 97.81% for the anger category.
The analysis results show that the emotional expressions of film actors and normal individuals differ. This study aims to develop a unique perspective by highlighting the distinctive characteristics of renowned actors through their emotional expressions.

References

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  • [30] R. Sonavane and P. Sonar, "Classification and segmentation of brain tumor using Adaboost classifier" in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016: IEEE, pp. 396-403.
  • [31] M. B. Devi and K. Amarendra, "Machine Learning-Based Application to Detect Pepper Leaf Diseases Using HistGradientBoosting Classifier with Fused HOG and LBP Features" in Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2021, 2021: Springer, pp. 359-369.
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Year 2024, Volume: 14 Issue: 2, 122 - 127

Abstract

References

  • [1] D. Ping Tian, "A review on image feature extraction and representation techniques" International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013.
  • [2] B. Chitradevi and P. Srimathi, "An overview on image processing techniques" International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 11, pp. 6466-6472, 2014.
  • [3] A. Eldem, H. Eldem, and A. Palali, "Görüntü işleme teknikleriyle yüz algılama sistemi geliştirme" Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 6, no. 2, pp. 44-48, 2017.
  • [4] S. Solak and U. Altınışık, "Görüntü işleme teknikleri ve kümeleme yöntemleri kullanılarak fındık meyvesinin tespit ve sınıflandırılması" Sakarya University Journal of Science, vol. 22, no. 1, pp. 56-65, 2018.
  • [5] S. Agduk and E. Aydemir, "Classification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods" Acta Informatica Pragensia, vol. 2022, no. 3, pp. 324-347, 2022.
  • [6] A. K. Sunal, TV ve sinemada Kemal Sunal güldürüsü. Marmara Universitesi (Turkey), 1998.
  • [7] Z. OKRAY and C. A. MEVLANA, "Selvi Boylum Al Yazmalım Filminin Göstergebilimsel Yöntem Bilimiyle Analizi" Uluslararası Beşeri Bilimler ve Eğitim Dergisi, vol. 5, no. 12, pp. 1216-1244, 2019.
  • [8] G. Kumar and P. K. Bhatia, "A detailed review of feature extraction in image processing systems" in 2014 Fourth international conference on advanced computing & communication technologies, 2014: IEEE, pp. 5-12.
  • [9] M. Kunaver and J. Tasic, "Image feature extraction-an overview" in EUROCON 2005-The International Conference on" Computer as a Tool", 2005, vol. 1: IEEE, pp. 183-186.
  • [10] Meena, G., et al. (2023). "Identifying emotions from facial expressions using a deep convolutional neural network-based approach." Multimedia Tools and Applications: 1-22.
  • [11] Aksoy, O. E. and S. Güney (2022). "Sentiment analysis from face expressions based on image processing using deep learning methods." Journal of Advanced Research in Natural and Applied Sciences 8(4): 736-752.
  • [12] Moung, E. G., et al. (2022). "Ensemble-based face expression recognition approach for image sentiment analysis." Int. J. Electr. Comput. Eng 12(3): 2588-2600.
  • [13] Gite, S., et al. (2024). "Real-Time Driver Sentiment Analysis Using Hybrid Deep Learning Algorithm." International Journal of Intelligent Systems and Applications in Engineering 12(6s): 735-748.
  • [14] Feng, X. F., et al. (2021). "An AI method to score celebrity visual potential from human faces." Shunyuan and Liu, Xiao and Srinivasan, Kannan and Lamberton, Cait Poynor, An AI Method to Score Celebrity Visual Potential from Human Faces (May 1, 2021).
  • [15] S. E. Bekhouche, A. Ouafi, A. Taleb-Ahmed, A. Hadid, and A. Benlamoudi, "Facial age estimation using bsif and lbp" arXiv preprint arXiv:1601.01876, 2016.
  • [16] T. I. Baig et al., "Classification of human face: Asian and non-Asian people" in 2019 International Conference on Innovative Computing (ICIC), 2019: IEEE, pp. 1-6.
  • [17] J.-S. Luo and D. C.-T. Lo, "Binary malware image classification using machine learning with local binary pattern" in 2017 IEEE International Conference on Big Data (Big Data), 2017: IEEE, pp. 4664-4667.
  • [18] Arslan, B., & Aydemir, E. (2023). Turkish Cinema Faces– Data set. Kaggle. https://www.kaggle.com/datasets/147e6c63e2bcb13f22ebbad54601e9a59c1556d85ed1251fdb6b9c6ecfc94f3d
  • [19] Chazzer (2022). Smiling or Not– Data set. Kaggle. https://www.kaggle.com/datasets/chazzer/smiling-or-not-face-data?resource=download
  • [20] Vaidya (2020). Natural Human Face Images for Emotion Recognition – Data set. Kaggle. https://www.kaggle.com/datasets/sudarshanvaidya/random-images-for-face-emotion-recognition
  • [21] Dalgın, G. T., & Daş, R. Sinema verilerinin Neo4j çizge veritabanı ile modellenmesi ve analizi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 15(1), 1-13.
  • [22] P. Kral and L. Lenc, "LBP features for breast cancer detection" in 2016 IEEE international conference on image processing (ICIP), 2016: IEEE, pp. 2643-2647.
  • [23] A. Gunay and V. V. Nabiyev, "Automatic age classification with LBP" in 2008 23rd international symposium on computer and information sciences, 2008: IEEE, pp. 1-4.
  • [24] Karanwal, S. and M. Diwakar (2023). "Triangle and orthogonal local binary pattern for face recognition." Multimedia Tools and Applications 82(23): 36179-36205.
  • [25] Brownlee, J. (2020, April 27). Histogram-Based Gradient Boosting Ensembles in Python. https://machinelearningmastery.com/histogram-based-gradient-boosting-ensembles/
  • [26] C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines" ACM transactions on intelligent systems and technology (TIST), vol. 2, no. 3, pp. 1-27, 2011.
  • [27] Y. Freund and R. E. Schapire, "A desicion-theoretic generalization of on-line learning and an application to boosting" in European conference on computational learning theory, 1995: Springer, pp. 23-37.
  • [28] M. Hossin and M. N. Sulaiman, "A review on evaluation metrics for data classification evaluations" International journal of data mining & knowledge management process, vol. 5, no. 2, p. 1, 2015.
  • [29] H. Dalianis and H. Dalianis, "Evaluation metrics and evaluation" Clinical Text Mining: secondary use of electronic patient records, pp. 45-53, 2018.
  • [30] R. Sonavane and P. Sonar, "Classification and segmentation of brain tumor using Adaboost classifier" in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016: IEEE, pp. 396-403.
  • [31] M. B. Devi and K. Amarendra, "Machine Learning-Based Application to Detect Pepper Leaf Diseases Using HistGradientBoosting Classifier with Fused HOG and LBP Features" in Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2021, 2021: Springer, pp. 359-369.
  • [32] N. V. Smirnov and A. S. Chernyshov, "Emotion recognition from facial images" in 2022 International Russian Automation Conference (RusAutoCon), 2022: IEEE, pp. 116-121.
There are 32 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Burak Arslan 0000-0003-1192-6012

Emrah Aydemir 0000-0002-8380-7891

Early Pub Date January 13, 2025
Publication Date
Submission Date January 24, 2024
Acceptance Date July 5, 2024
Published in Issue Year 2024 Volume: 14 Issue: 2

Cite

APA Arslan, B., & Aydemir, E. (2025). Analysis of Emotional Authenticity Displayed by Film Actors Using Image Processing Techniques. European Journal of Technique (EJT), 14(2), 122-127. https://doi.org/10.36222/ejt.1425158

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