İmge Kümeleriyle Yüz Tanıma için Otomatik Önişleme

Year 2019, Volume: 11 Issue: 2, 464 - 473, 30.06.2019

Hasan Serhan Yavuz , Meltem Seyirt

https://doi.org/10.29137/umagd.510731

Abstract

Otomatik yüz tanıma süreci son yıllarda
popülerliğini arttırmış bir konudur. İmge tabanlı yaklaşımların hâkim olduğu
yüz tanıma süreci, kamera ve hesaplama teknolojilerinin gelişimiyle yerini
video tabanlı yaklaşımlara bırakmaktadır. Video tabanlı yüz tanıma
uygulamalarında, özellikle kişilerin farklı aydınlatma veya cepheden, yandan
görünüm vb. farklı pozlar içeren imge kümelerinin eşleştirilmesi zorluklar
içermektedir. Bu çalışmada, özellikle aydınlatma ve poz çeşitliliklerinin var
olduğu durumlarda, küme tabanlı yüz tanıma yöntemlerinin başarımlarının nasıl
iyileştirilebileceği araştırılmıştır. Ön işleme basamağında, aydınlatma
farklılıkları giderildikten sonra imgeler öncelikle yüz pozuna göre
sınıflandırılmıştır. Pozlara göre ayrıştırılan yüzler, sınıf içi değişimlerinin
azaltılması için ilgili pozun şablonuna hizalanmıştır. Yapılan deneyler
sonucunda, önişleme basamağında önerilen otomatik poz hizalama yöntemi
kullanıldığında, video tabanlı yüz tanıma deneylerinin başarım oranlarında
gelişmeler sağlandığı tespit edilmiştir.

Keywords

yüz sezimi, yüz hizalama, yüz tanıma, imge kümeleri

Autonomous Preprocessing for Image Set Based Face Recognition

Abstract

Automatic face recognition process has become a popular topic in recent years. The facial recognition process, where previously single-image based methods were more common, has started to leave its place in video-based approaches by the development of camera and computing technologies. In video based recognition applications, it becomes more difficult to match the image sets of the same person whose frames captured under different illumination conditions or when the compared frames include different face poses such as frontal versus profile. In this study, we investigate how to improve the accuracies of set based face recognition methods in case of lighting and face pose variations. At the pre-processing stage, after the illumination differences are refined, the images are firstly classified according to face exposure. The faces that are separated according to the poses are aligned to the corresponding canonical pose patterns to reduce intra class variations. Experimental results demonstrate that set based recognition methods give higher correct recognition rates when the proposed methodology schemes have been applied as a preprocessing stage.

Keywords

face detection, face alignment, face recognition, image sets

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