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

Öz

   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. 

Anahtar Kelimeler

• 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

Kaynakça

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