Classification of Different Age Groups of People by Using Deep Learning
Yazarlar (2)
Doç. Dr. Özkan İNİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale |
| Dergi Adı | Journal of New Results in Science |
| Dergi ISSN | 1304-7981 |
| Dergi Tarandığı Indeksler | WorldCat, Electronic Journals Library EZB, ResearchBib, Publons, Journal Factor, Journal Index, CiteFactor, Cosmos Impact Factor, Global Impact Factor, General Impact Factor, International, Innovative Journal Impact Factor, Impact Factor Services for International Journals, International Accreditation and Research Council IARC |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2018 |
| Cilt No | 7 |
| Sayı | 3 |
| Sayfalar | 9 / 16 |
| Makale Linki | https://dergipark.org.tr/tr/download/article-file/582149 |
| Özet |
| The Purpose of this study is to classify human images of different age groups with VggNet which is one of the Deep Learning (DL) models. Artificial intelligence, machine learning and computer vision have been carried out in recent years at very advanced level. Undoubtedly, it is a great contribution of DL in the rapid progress of these studies. Although DL foundational is based on past history, it has become popular in the imageNet competition held in 2012. This is because the top-5 error rate of 26.1% for visual object description has fallen to 15.3% for the first time with a sharp decline that year with DL. The Convolution Neural Network (CNN) is basis of DL models. It is basically composed of 4 layers. These are Convolution Layer, ReLu Layer, Pooling Layer and Full Connected Layer. DL models are designed using different numbers of these layers. In this study, people are divided into 12 classes according to age groups. These classes are man, woman, man face, woman face, old man, old woman, old man face, old woman face, boy, girl, boy face, girl face respectively. A new data set was created for people in 12 different age categories. For Each class 150 and totally 1800 images were collected. 90% of these images were used for training and the remaining 10% were used for testing. VggNet was trained with this data set. As a result of the study, it was seen that people in different age groups were estimated with 78.5% accuracy with VggNet model. DL models need to be trained with large data required. But it has been seen that training success has achieved a certain value with little data. |
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