Bu kaydın yasal hükümlere uygun olmadığını düşünüyorsanız lütfen sayfa sonundaki Hata Bildir bağlantısını takip ederek bildirimde bulununuz. Kayıtlar ilgili üniversite yöneticileri tarafından eklenmektedir. Nadiren de olsa kayıtlarla ilgili hatalar oluşabilmektedir. MİTOS internet üzerindeki herhangi bir ödev sitesi değildir!

Age And Gender Prediction From 3d-Body And Face Images

Oluşturulma Tarihi: 04-09-2018

Niteleme Bilgileri

Tür: Tez

Alt Tür: Doktora

Yayınlanma Durumu: Yayınlanmamış

Dosya Biçimi: PDF

Dil: Türkçe

Konu(lar): Mühendislik (Genel). İnşaat mühendisliği (General),

Yazar(lar): Çalaman, Seda (Yazar),

Emeği Geçen(ler): Şengül, Gökhan (Danışman),

Anahtar Kelimeler

Age prediction, gender prediction, pattern recognition, neural network, deep learning


Özet

The biometric data collected from individuals provide an array of information about any population and their environment which can be used in several areas, including transportation (busses, ferries, railways, etc), shopping malls, public areas, sports centers, museums, supermarkets, libraries, etc., not to mention security applications. In detail, this biometric data is related with identity, gender, race, height, weight, and eye and hair color of the person. In this thesis, an image processing-based system to predict the two major aspects, age range and genders of people is developed and integrated as a software tool. A standard RGB camera is used to acquire face images, while a 3D camera is used for body information. To predict the gender and age of each individual, statistical pattern recognition algorithms, deep learning and neural network-based approaches are utilized. For statistical methods, LBP and HOG methods are applied on face images to extract features, then KNN and SVM classification methods are applied as classifiers.  Convolutional neural network is used to predict age range of people and the comparison between statistical methods and convolutional neural networks are presented. For age prediction, from face images, statistical methods results yielding a top accuracy of 40.1%; whereas, the best accuracy obtained from CNN deep learning is 59.1%. In addition, 3D body information is used for gender and age prediction by applying statistical and neural network methods. These methods show to improve the gender prediction rate by up to 99.26% and age
iv prediction by 99.41% for the whole-body information. The upper-body and lowerbody parts are also examined separately to predict the age and gender of the each individual.


İçindekiler



Açıklamalar



Haklar



Notlar



Kaynakça


Atıf Yapanlar

Gözat Sayfasına Dön

 

Sosyal Medya ve Araçlar

İstatistikler

  • Kayıt
    • Bu ay: 12
    • Toplam: 2351
  • Online
    • Ziyaretçi: 27
    • Üye: 0
    • Toplam: 27

Detaylı İstatistikler