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Recognition of Microexpressions Using Xception Deep Learning Model and Gabor Filters with RFECV-SVM Algorithm

Year 2023, Volume: 13 Issue: 4, 2339 - 2352, 01.12.2023
https://doi.org/10.21597/jist.1252556

Abstract

Micro Expression (ME) is the leakage that occurs when people try to mask their involuntary and uncontrolled emotional response to an event in a risky environment. Because the person experiencing the emotion at risk tries to suppress it, its reflection on the face occurs in a low intensity, a specific region, and a very short time. Since the expression emerges involuntarily, it is not fake but completely natural. Thanks to the correct detection of these natural expressions, it can be used effectively in many fields such as forensics, clinical, and education. This study used preprocessing, feature extraction, feature selection, and classification tasks in the framework created for the ME recognition target. CASME-II, one of the literature's most widely used publicly available ME datasets, was used in the proposed framework. In the preprocessing stage, onset and apex Frames are taken from the image sequence of each video clip to be used in optical flow algorithms. These two frames obtained horizontal and vertical optical flow images of Farneback, TV-L1 Dual, and TV-L1. Then the features of these optical flow images were obtained using the convolutional neural network (CNN) model Xception and the traditional Gabor model. Recursive feature elimination with a cross-validation (RFECV) feature selection algorithm was used to filter the distinctive ones of these features. Finally, the SVC Linear classifier divided the filtered ME features into three classes: positive, negative, and surprise. The results obtained from the proposed ME framework showed an accuracy rate of 0.9248.

References

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Xception Derin Öğrenme Modeli ve Gabor Filtreleri ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması

Year 2023, Volume: 13 Issue: 4, 2339 - 2352, 01.12.2023
https://doi.org/10.21597/jist.1252556

Abstract

Mikro ifade (Mİ), insanların riskli bir ortamda bir olaya karşı istemsiz ve kontrolsüz duygusal tepkilerini gizlemeye çalıştıklarında ortaya çıkan sızıntıdır. Duyguyu yaşayan kişi risk altında bunu bastırmaya çalıştığı için yüze yansıması düşük yoğunlukta, belirli bir bölgede ve çok kısa sürede gerçekleşir. İfade istemsizce ortaya çıktığı için sahte değil tamamen doğal olmaktadır. Bu doğal ifadelerin doğru tespiti sayesinde adli, klinik, eğitim gibi birçok alanda etkili bir şekilde kullanılması sağlanabilir. Bu çalışmada Mİ tanıma hedefi için oluşturulan model yapısında sırasıyla önişleme, öznitelik çıkarma, öznitelik seçme ve sınıflandırma görevleri kullanılmıştır. Önerilen model yapısında literatürde en çok kullanılan, kamuya açık Mİ veri setlerinden CASME-II kullanılmıştır. Ön işleme aşamasında Optik Akış algoritmalarında kullanılmak üzere her bir video klipin görüntü dizisinden başlangıç (onset) ve tepe (apex) kareleri seçilir. Bu iki kare kullanılarak Farneback, TV-L1 Dual ve TV-L1 e ait yatay ve dikey optik akış görüntüleri elde edilmiş, ardından bu optik akış görüntüleri evrişimsel sinir ağı (ESA) modeli olan Xception ve geleneksel model olan Gabor modelleri kullanılarak görüntülere ait öznitelikler elde edilmiştir. Elde edilen bu özniteliklere ait ayırt edici olanları filtrelemek için çapraz doğrulama ile özyinelemeli özellik eleme (ÇDÖÖE) öznitelik seçim algoritması kullanılmıştır. Son olarak doğrusal destek vektör sınıflandırıcısı (DVS), filtrelenmiş Mİ özniteliklerini pozitif, negatif ve sürpriz olmak üzere üç sınıfa ayırmıştır. Önerilen Mİ model yapısından elde edilen sonuçlar 0.9248 doğruluk oranı başarısı göstermiştir.

References

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  • Basaran, E., Cömert, Z., Çelik, Y., Budak, Ü., & Sengür, A. (2020). Otitis media diagnosis model for tympanic membrane images processed in two-stage processing blocks. IOP Sci, 14, 1–27.
  • Ben, X., Ren, Y., Zhang, J., Wang, S.-J., Kpalma, K., Meng, W., & Liu, Y.-J. (2021). Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1. Tarihinde adresinden erişildi https://doi.org/10.1109/TPAMI.2021.3067464
  • Bozkurt, F. Skin lesion classification on dermatoscopic images using effective data augmentation and pre-trained deep learning approach. Multimed Tools Appl 82, 18985–19003 (2023).
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  • Gan, Y S, See, J., Khor, H.-Q., Liu, K.-H., & Liong, S.-T. (2022). Needle in a Haystack: Spotting and recognising micro-expressions “in the wild”. Neurocomputing, 503, 283–298. Tarihinde adresinden erişildi https://doi.org/https://doi.org/10.1016/j.neucom.2022.06.101
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  • Gao, T., Zhao, X. M., Chen, T., Liu, Z. W., & Ni, C. (2017). Face description based on adaptive local weighted Gabor comprehensive histogram feature. Multimedia Tools and Applications, 76(10), 12893–12916. Tarihinde adresinden erişildi https://doi.org/10.1007/s11042-016-3701-y
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  • Jirik, M., Ryba, T., & Zelezny, M. (2011). Texture based segmentation using graph cut and Gabor filters. Pattern Recognition and Image Analysis, 21, 258–261.
  • Karcioğlu, A. A., & Aydin, T. (2019, April). Sentiment analysis of Turkish and english twitter feeds using Word2Vec model. In 2019 27th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE
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  • Li, Y., Huang, X., & Zhao, G. (2020). Joint Local and Global Information Learning With Single Apex Frame Detection for Micro-Expression Recognition. IEEE Transactions on Image Processing, 30, 249–263.
  • Lin, C., Long, F., Huang, J., & Li, J. (2018). Micro-Expression Recognition Based on Spatiotemporal Gabor Filters. Içinde 2018 Eighth International Conference on Information Science and Technology (ICIST) (ss. 487–491). Tarihinde adresinden erişildi https://doi.org/10.1109/ICIST.2018.8426088
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  • Liu, K.-H., Jin, Q.-S., Xu, H.-C., Gan, Y.-S., & Liong, S.-T. (2021). Micro-expression recognition using advanced genetic algorithm. Signal Processing: Image Communication, 93, 116153. Tarihinde adresinden erişildi https://doi.org/https://doi.org/10.1016/j.image.2021.116153
  • Liu, N., Liu, X., Zhang, Z., Xu, X., & Chen, T. (2020). Offset or Onset Frame: A Multi-Stream Convolutional Neural Network with CapsuleNet Module for Micro-expression Recognition. Içinde 2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) (ss. 236–240). IEEE.
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  • Mustaqim, A. Z., Adi, S., Pristyanto, Y., & Astuti, Y. (2021). The Effect of Recursive Feature Elimination with Cross-Validation (RFECV) Feature Selection Algorithm toward Classifier Performance on Credit Card Fraud Detection. Içinde 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST) (ss. 270–275). Tarihinde adresinden erişildi https://doi.org/10.1109/ICAICST53116.2021.9497842
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  • Porter, S., Ten Brinke, L., & Wallace, B. (2012). Secrets and lies: Involuntary leakage in deceptive facial expressions as a function of emotional intensity. Journal of Nonverbal Behavior, 36(1), 23–37.
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There are 52 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Bilgisayar Mühendisliği / Computer Engineering
Authors

Mehmet Zahit Uzun 0000-0002-6180-5860

Erdal Başaran 0000-0001-8569-2998

Yuksel Celık 0000-0002-7117-9736

Early Pub Date November 30, 2023
Publication Date December 1, 2023
Submission Date February 21, 2023
Acceptance Date August 23, 2023
Published in Issue Year 2023 Volume: 13 Issue: 4

Cite

APA Uzun, M. Z., Başaran, E., & Celık, Y. (2023). Xception Derin Öğrenme Modeli ve Gabor Filtreleri ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması. Journal of the Institute of Science and Technology, 13(4), 2339-2352. https://doi.org/10.21597/jist.1252556
AMA Uzun MZ, Başaran E, Celık Y. Xception Derin Öğrenme Modeli ve Gabor Filtreleri ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması. J. Inst. Sci. and Tech. December 2023;13(4):2339-2352. doi:10.21597/jist.1252556
Chicago Uzun, Mehmet Zahit, Erdal Başaran, and Yuksel Celık. “Xception Derin Öğrenme Modeli Ve Gabor Filtreleri Ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması”. Journal of the Institute of Science and Technology 13, no. 4 (December 2023): 2339-52. https://doi.org/10.21597/jist.1252556.
EndNote Uzun MZ, Başaran E, Celık Y (December 1, 2023) Xception Derin Öğrenme Modeli ve Gabor Filtreleri ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması. Journal of the Institute of Science and Technology 13 4 2339–2352.
IEEE M. Z. Uzun, E. Başaran, and Y. Celık, “Xception Derin Öğrenme Modeli ve Gabor Filtreleri ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması”, J. Inst. Sci. and Tech., vol. 13, no. 4, pp. 2339–2352, 2023, doi: 10.21597/jist.1252556.
ISNAD Uzun, Mehmet Zahit et al. “Xception Derin Öğrenme Modeli Ve Gabor Filtreleri Ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması”. Journal of the Institute of Science and Technology 13/4 (December 2023), 2339-2352. https://doi.org/10.21597/jist.1252556.
JAMA Uzun MZ, Başaran E, Celık Y. Xception Derin Öğrenme Modeli ve Gabor Filtreleri ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması. J. Inst. Sci. and Tech. 2023;13:2339–2352.
MLA Uzun, Mehmet Zahit et al. “Xception Derin Öğrenme Modeli Ve Gabor Filtreleri Ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması”. Journal of the Institute of Science and Technology, vol. 13, no. 4, 2023, pp. 2339-52, doi:10.21597/jist.1252556.
Vancouver Uzun MZ, Başaran E, Celık Y. Xception Derin Öğrenme Modeli ve Gabor Filtreleri ile ÇDÖÖE-DVM Algoritması Kullanılarak Mikro İfadelerin Tanınması. J. Inst. Sci. and Tech. 2023;13(4):2339-52.