Derin Öğrenme ile Yüz Tanıma ve Duygu Analizi

Year 2021, Issue: 31, 764 - 770, 31.12.2021

Yaşar Safalı Erdinç Avaroğlu

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

Cited By: 1

Abstract

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.

Keywords

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

Supporting Institution

Mersin Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

2019-2-TP2-3532

Thanks

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

Abstract

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%.

Keywords

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

Project Number

2019-2-TP2-3532

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