YEREL İKİLİ ÖRÜNTÜ VE YÖNLÜ GRADYANT HİSTOGRAMI KULLANILARAK YÜZ GÖRÜNTÜLERİNDEN CİNSİYET TAHMİNİ

Year 2018, Volume: 7 Issue: 1, 14 - 22, 31.01.2018

Tariq Khalıfa Gökhan Şengül

https://doi.org/10.28948/ngumuh.383746

Cited By: 2

Abstract

   Yüz görüntülerinden cinsiyet tahmini;
insan-bilgisayar arayüzü, müşteri bilgilerinin ölçülmesi, erişim kontrolü gibi
birçok alanda kullanılmaktadır. Bunlara ek olarak cinsiyet tahminin; güvenlik
sistemleri, biyometrik kimlik doğrulama, medikal görüntüleme sistemleri,
demografik çalışmalar, içerik tabanlı arama, izleme sistemleri gibi alanlarda
da uygulanma potansiyeli bulunmaktadır. Bu çalışmada yüz görüntülerinden
cinsiyet tahmini için yerel ikili örüntü (local binary patterns) ve yönlü
gradyant histogramını özellik çıkarıcı ve sınıflandırıcı olarak da k-en Yakın Komşuluk ve Destek Vektör
Makinelerini kullanan bir sistem önerilmiştir. Önerilen sistem FERET ve UTD
veritabanlarında test edilmiştir. Testler esnasında birini dışarıda bırakma
çapraz geçerleme tekniği uygulanmıştır. Elde edilen sonuçlar tatmin edici
seviyededir. 

Keywords

Yüz görüntüleri, cinsiyet tahmini, yerel ikili örüntü, yönlü granyant histogramı

GENDER PREDICTION FROM FACIAL IMAGES USING LOCAL BINARY PATTERNS AND HISTOGRAMS OF ORIENTED GRADIENTS TRANSFORMATIONS

Abstract

   Gender
prediction from facial images can be used in a large number of applications
including human-computer interaction, customer information measurement, access
control, etc. Furthermore, it can substantially effect on many fields, such as
security systems, biometric authentication, medical imaging systems,
demographic studies, content based searching, and surveillance system. In this
study, we proposed to use Local Binary Patterns (LBP) and Histograms of
Oriented Gradients (HOG) as the feature extractor and k-Nearest Neighbor
(k-NN) and Support Vector Machine (SVM) as the classifier in order to
predict the gender of the people from facial images. We tested the proposed
method in FERET and UTD databases. We used leave-one-out approach as the cross
validation technique. The results are promising.

Keywords

Facial images, gender prediction, Local Binary Patterns, Histograms of Oriented Gradients

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