Derin Öğrenme ile Yüz Tanıma ve Duygu Analizi

Yıl 2021, Sayı: 31, 764 - 770, 31.12.2021

Yaşar Safalı , Erdinç Avaroğlu

https://doi.org/10.31590/ejosat.1010450

Cited By: 1

Öz

Kişilerin davranışlarına, fiziksel özelliklerine bağlı olarak geliştirilen biyometrik sitemler son yıllarda aktif olarak kullanılmaktadır. Kişinin benzersiz özelliklerine dayanan biyometrik sistemler içerisinde yüz tanıma fiziksel temasa gerek duymaması sebebi ile önemli bir yer kaplamaktadır. Bu çalışmada derin öğrenme tabanlı yüz tanıma ve yüz ifadesi tanıma uygulaması gerçekleştirilmiştir. VGG-16, AlexNet ve ZF Net mimarileri ile geliştirilen modeller eğitilerek başarı oranları karşılaştırılmıştır. En başarılı model %92,03 başarı oranı ile VGG-16 mimarisi referans alınarak geliştirilen model olmuştur.

Anahtar Kelimeler

Yüz tanıma, Yüz ifadesi tanıma, derin öğrenme, Biyometrik sistemler.

Destekleyen Kurum

Mersin Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Proje Numarası

2019-2-TP2-3532

Teşekkür

Bu çalışma, Mersin Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından 2019-2-TP2-3532 numaralı proje olarak desteklenmiştir. Destekleri için Mersin Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi’ne teşekkür ederim.

Face Recognition and Emotion Analysis with Deep Learning

Öz

Biometric systems developed depending on the behavior and physical characteristics of individuals have been actively used in recent years. Facial recognition occupies an important place among biometric systems based on the unique characteristics of the person, as it does not require physical contact. In this study, facial recognition and facial expression recognition based on deep learning were implemented. Models developed with VGG-16, AlexNet and ZF Net architectures were trained and their success rates were compared. The most successful model was the model developed based on VGG-16 architecture with a success rate of 92.03%.

Anahtar Kelimeler

Face recognition, facial expression recognition, deep learning, Biometric systems.

Proje Numarası

2019-2-TP2-3532

Kaynakça

• Goodfellow, I, Y Bengio, A Courville, and Y Bengio. 2016. “Deep Bilgic, Ahmet, Onur Can Kurban, and Tulay Yildirim. 2017. “Face Recognition Classifier Based on Dimension Reduction in Deep Learning Properties.” In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Institute of Electrical and Electronics Engineers Inc.
• Dehghan, Afshin, Enrique G Ortiz Guang, Shu Syed, and Zain Masood. DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Networks. https://www.sighthound.com/products/cloud (December 14, 2020).
• Dürr, Oliver et al. 2015. “Deep Learning on a Raspberry Pi for Real Time Face Recognition TubeCam: A New System to Detect Small Mammals (Foremost Mustelids and Dormice) View Project Speaker Diarization View Project Deep Learning on a Raspberry Pi for Real Time Face Recognition.” researchgate.net. https://www.researchgate.net/publication/279537625 (December 18, 2020).
• Goodfellow, I, Y Bengio, A Courville, and Y Bengio. 2016. “Deep Learning.” https://doi.org/10.4258/hir.2016.22.4.351 (January 3, 2021).
• Hjelmås, Erik. hig.no Feature-Based Face Recognition. http://www.hig.no/~erikh/papers/nobim2000.pdf (December 18, 2020).
• Kalocsai, P, C von der Malsburg, J Horn - Image and Vision Computing, and undefined 2000. “Face Recognition by Statistical Analysis of Feature Detectors.” Elsevier. https://www.sciencedirect.com/science/article/pii/S0262885699000517?casa_token=H2O15WOWp8AAAAAA:LjVzRWDnOUdLj-oM7Pq4-JOM6jPmDySyCEictAB_iKVYec6n-aMMtmJXNAkJ1muYdQFJ1zKUES2q (December 18, 2020).
• Kirby N D L Sirovich, M A. 1990. 12 IEEE Trans. Pattern Anal. Ma-chine Intell Using Polygons to Recognize and Locate Partially Occluded Objects. Pion Limited. https://ieeexplore.ieee.org/abstract/document/41390/ (December 18, 2020).
• Koushik, Jayanth. 2016. “Understanding Convolutional Neural Networks.” http://arxiv.org/abs/1605.09081 (January 3, 2021).
• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2017. “ImageNet Classification with Deep Convolutional Neural Networks.” Communications of the ACM.
• Kumar, Ashu, Amandeep Kaur, and · Munish Kumar. 2019. “Face Detection Techniques: A Review.” Artificial Intelligence Review 52: 927–48. https://doi.org/10.1007/s10462-018-9650-2 (December 18, 2020).
• Lau, Jey Han, and Timothy Baldwin. 2016. “An Empirical Evaluation of Doc2vec with Practical Insights into Document Embedding Generation.” In Association for Computational Linguistics (ACL), 78–86.
• Özkan, İ, E Ülker - Gaziosmanpaşa Bilimsel Araştırma, and undefined 2017. “Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri.” pdfs.semanticscholar.org. https://pdfs.semanticscholar.org/e2b2/4cc0c4c529d341c450bc18f949968c7cac8d.pdf (January 3, 2021).
• Pervan, Nergis. 2019. ANKARA ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜ YÜKSEK LİSANS TEZİ DERİN ÖĞRENME YAKLAŞIMLARI KULLANARAK TÜRKÇE METİNLERDEN ANLAMSAL ÇIKARIM YAPMA. http://dspace.ankara.edu.tr/xmlui/handle/20.500.12575/41714 (January 3, 2021).
• Salunke, Vibha V., and C. G. Patil. 2018. “A New Approach for Automatic Face Emotion Recognition and Classification Based on Deep Networks.” In 2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017, Institute of Electrical and Electronics Engineers Inc.
• Satish, Anila, Nanjundappan Devarajan, S Anila, and N Devarajan. 2010. researchgate.net Simple and Fast Face Detection System Based on Edges IoT Based Automatic Farm Management System Using Wireless Sensor Networks View Project Powerful and Dominating Woman View Project Simple and Fast Face Detection System Based on Edges. https://www.researchgate.net/publication/225292501 (December 18, 2020).
• Simonyan, Karen, and Andrew Zisserman. 2015. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, International Conference on Learning Representations, ICLR.
• Tüfekçi, Mustafa, and Doç Fatih Karpat. “Derin Öğrenme Mimarilerinden Konvolüsyonel Sinir Ağları (CNN) Üzerinde Görüntü İşleme-Sınıflandırma Kabiliyetininin Arttırılmasına Yönelik Yapılan Çalışmaların İncelenmesi.” www.set-science.com (January 2, 2021).
• Vatsa, M, R Singh, and A Majumdar. 2018. “Deep Learning in Biometrics.” https://books.google.com/books?hl=tr&lr=&id=rGhQDwAAQBAJ&oi=fnd&pg=PP1&dq=Vatsa,+M.,+Singh,+R.+and+Majumdar,+A.,+2018.+Deep+Learning+in+Biometrics,+CRC+Press+Taylor+%26+Francis+Group,+New+York&ots=e69JCxR4g3&sig=J4s94pKBX3s_HGKCMsSzsyyn3pU (December 12, 2020).
• Wiskott, Laurenz, Jean Marc Fellous, Norbert Krüger, and Christoph Der Von Malsburg. 1997. “Face Recognition by Elastic Bunch Graph Matching.” IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7): 775–79.
• Zeiler, Matthew D., and Rob Fergus. 2014. “Visualizing and Understanding Convolutional Networks.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 818–33.
• Zhang, H, Y Xie, C Xu - Proceedings 2011 International, and undefined 2011. “A Classifier Training Method for Face Detection Based on AdaBoost.” ieeexplore.ieee.org. https://ieeexplore.ieee.org/abstract/document/6199306/ (December 18, 2020).
• Learning.” https://doi.org/10.4258/hir.2016.22.4.351 (January 3, 2021).
• Koushik, Jayanth. 2016. “Understanding Convolutional Neural Networks.” http://arxiv.org/abs/1605.09081 (January 3, 2021).
• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2017. “ImageNet Classification with Deep Convolutional Neural Networks.” Communications of the ACM.
• Özkan, İ, E Ülker - Gaziosmanpaşa Bilimsel Araştırma, and undefined 2017. “Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri.” pdfs.semanticscholar.org. https://pdfs.semanticscholar.org/e2b2/4cc0c4c529d341c450bc18f949968c7cac8d.pdf (January 3, 2021).
• Pervan, Nergis. 2019. ANKARA ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜ YÜKSEK LİSANS TEZİ DERİN ÖĞRENME YAKLAŞIMLARI KULLANARAK TÜRKÇE METİNLERDEN ANLAMSAL ÇIKARIM YAPMA. http://dspace.ankara.edu.tr/xmlui/handle/20.500.12575/41714 (January 3, 2021).
• Simonyan, Karen, and Andrew Zisserman. 2015. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, International Conference on Learning Representations, ICLR.
• Tüfekçi, Mustafa, and Doç Fatih Karpat. “Derin Öğrenme Mimarilerinden Konvolüsyonel Sinir Ağları (CNN) Üzerinde Görüntü İşleme-Sınıflandırma Kabiliyetininin Arttırılmasına Yönelik Yapılan Çalışmaların İncelenmesi.” www.set-science.com (January 2, 2021).
• Zeiler, Matthew D., and Rob Fergus. 2014. “Visualizing and Understanding Convolutional Networks.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 818–33.