Facial Emotion Recognition With VGG-11 Based On Classification Algorithms

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 359 - 365, 20.10.2021

Kenan Donuk Davut Hanbay

https://doi.org/10.53070/bbd.990613

Cited By: 1

Abstract

Facial emotional expressions are a non-verbal communication tool in people's communication with each other. These expressions give information about people's thoughts. In the light of this information, studies are carried out in many areas such as customer satisfaction, detection of mental disorders, autism, detection of lies and fear. There are many traditional and deep learning-based methods for emotion recognition task. In the study, FER2013 dataset and VGG-11, one of the deep learning-based architectures, were used for emotion recognition. Test accuracy of 68.32% was achieved with the VGG-11 architecture. In this article, the effect of classification methods applied to the feature layer of the VGG-11 architecture on emotion recognition accuracy is examined.

Keywords

Vgg-11, facial emotion recognition, fer2013

Project Number

FDK-2020-2110

Sınıflandırma Algoritmalarına Dayalı VGG-11 ile Yüzde Duygu Tanıma

Abstract

Yüz duygu ifadeleri insanların birbirleriyle olan iletişiminde sözlü olmayan bir iletişim aracıdır. Bu ifadeler insanların düşünceleri hakkında bilgiler vermektedir. Bu bilgiler ışığında müşteri memnuniyeti, zihinsel bozuklukların tespiti, otizm, yalan ve korku tespiti gibi birçok alanda çalışmalar yapılmaktadır. Duygu tanıma görevi için yapılmış geleneksel ve derin öğrenme tabanlı birçok yöntem mevcuttur. Yapılan çalışmada duygu tanıma için FER2013 veriseti ve derin öğrenme tabanlı mimarilerden VGG-11 kullanılmıştır. VGG-11 mimarisi ile %68.32’lik test doğruluğu elde edilmiştir. Çalışmada VGG-11 mimarisinin özellik katmanına uygulanan sınıflandırma yöntemlerinin duygu tanıma doğruluğuna etkisi incelenmiştir.

Keywords

Vgg-11, yüz duygu tanıma, fer2013

Supporting Institution

İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP)

Project Number

FDK-2020-2110

Thanks

Bu çalışma İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP) tarafından FDK-2020-2110 kodlu proje ile desteklenmiştir.

References

• Mahmoudi MA, Chetouani A, Boufera F, Tabia H. (2020) Improved Bilinear Model for Facial Expression Recognition. Communications in Computer and Information Science, 1322 CCIS, pp.47-59.
• Bouzakraoui MS, Sadiq A, Alaoui AY (2020) Customer satisfaction recognition based on facial expression and machine learning techniques. Advances in Science, Technology and Engineering Systems 5(4):594–599.
• Mukhopadhyay M, Pal S, Nayyar A, Pramanik PKD, Dasgupta N, Choudhury P. (2020) Facial Emotion Detection to Assess Learner’s State of Mind in an Online Learning System. ACM International Conference Proceeding Series, pp.107-115.
• ShanthaShalini K, Jaichandran R, Leelavathy S, Raviraghul R, Ranjitha J, Saravanakumar N (2021) Facial Emotion Based Music Recommendation System using computer vision and machine learning techiniques. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12(2):912–917.
• Gao Z, Zhao W, Liu S, Liu Z, Yang C, Xu Y (2021) Facial Emotion Recognition in Schizophrenia. Frontiers in Psychiatry.
• Simcock G, McLoughlin LT, Regt TD, Broadhouse KM, Beaudequin D, Lagopoulos J, Hermens DF (2020) Associations between Facial Emotion Recognition and Mental Health in Early Adolescence. International Journal of Environmental Research and Public Health 17(1).
• Zahara L, Musa P, Prasetyo Wibowo E, Karim I, Bahri Musa S. (2020) The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face Using the Convolutional Neural Network (CNN) Algorithm based Raspberry Pi. 5th International Conference on Informatics and Computing (ICIC 2020).
• Khaireddin Y, Chen Z (2021) Facial Emotion Recognition: State of the Art Performance on FER2013.
• Shafira SS, Ulfa N, Wibawa HA, Rismiyati. (2019) Facial Expression Recognition Using Extreme Learning Machine. ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings.
• Daihong J, Yuanzheng H, Lei D, Jin P (2021) Facial Expression Recognition Based on Attention Mechanism. Scientific Programming.
• Dong TN, Van L, Bao PT (2021) Facial Expression Recognition Using Multi-deep Convolutional Neural Network Encoders with Support Vector Machines. International Journal of Machine Learning and Computing 11(5):345–349.
• Mollahosseini A, Chan D, Mahoor MH. (2016) Going deeper in facial expression recognition using deep neural networks. 2016 IEEE Winter Conference on Applications of Computer Vision, (WACV 2016).
• Simonyan K, Zisserman A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings.
• Gan Y, Yang J, Lai W. (2019) Video object forgery detection algorithm based on VGG-11 convolutional neural network. Proceedings - 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS 2019), pp.575–580.
• Wu M, Ma W, Li Y, Zhao X. (2020) The Optimization Method of Knowledge Distillation Based on Model Pruning. Proceedings - 2020 Chinese Automation Congress (CAC 2020), pp.1386–1390.
• Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B ve ark. (2013) Challenges in Representation Learning: A report on three machine learning contests. Neural Networks 64:59–63.
• Breiman L (2001) Random Forests. Machine Learning 2001 45(1):5-32.
• Siji George CG, Sumathi B (2021) Genetic Algorithm Based Hybrid Model Of convolutional Neural Network And Random Forest Classifier For Sentiment Classification. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12(2):3216–3223.
• Ooka T, Johno H, Nakamoto K, Yoda Y, Yokomichi H, Yamagata Z (2021) Random forest approach for determining risk prediction and predictive factors of type 2 diabetes: large-scale health check-up data in Japan. BMJ Nutrition, Prevention & Health.
• Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning 20(3):273–297.
• Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A (2020) A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 408:189–215.
• Saigal P, Khanna V (2020) Multi-category news classification using Support Vector Machine based classifiers. SN Applied Sciences 2(3):1-12.
• Cover TM, Hart PE (1967) Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13(1):21-27.
• Taunk K, De S, Verma S, Swetapadma A. (2019) A brief review of nearest neighbor algorithm for learning and classification. 2019 International Conference on Intelligent Computing and Control Systems (ICCS 2019), pp.1255–1260.
• Quinlan JR (1986) Induction of Decision Trees. Machine Learning 1:81-106.
• Bastos NS, Marques BP, Adamatti DF, Billa CZ (2020) Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude. Computational Intelligence and Neuroscience.
• Liu S, Yang Z, Li Y, Wang S (2020) Decision tree-based sensitive information identification and encrypted transmission system. Entropy 22(2).
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O ve ark. (2011) Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12(85):2825-2830.