Facial component segmentation using convolutional neural network

Facial components are important for many face image analysis applications. Facial component segmentation is a challenging task due to variations in illumination conditions, pose, scale, skin color etc. Deep learning is a novel branch of machine learning, very efficient in solving complex problems. I...

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Bibliographic Details
Published inThe online journal of science and technology Vol. 8; no. 2; pp. 84 - 88
Main Authors Yolcu Öztel,Gözde, Öztel,İsmail, Kazan Oğuz,Serap, Öz,Cemil, Bunyak,Filiz
Format Journal Article
LanguageEnglish
Published Sakarya Üniversitesi Yayınları 01.04.2018
Subjects
Bilim-Teknoloji
İstanbul
Türkiye
Facial Feature Extraction
Convolutional Neural Network
Facial Image Segmentation
Deep Learning
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Summary:Facial components are important for many face image analysis applications. Facial component segmentation is a challenging task due to variations in illumination conditions, pose, scale, skin color etc. Deep learning is a novel branch of machine learning, very efficient in solving complex problems. In this study, we developed a deep Convolutional Neural Network (CNN) to automatically segment facial components in face images. The network has been trained with face images in Radboud face database. Training labels have been created using Face++ SDK. The developed CNN produces a segmentation mask where mouth, eyes, and eyebrows components of the face are marked as foreground. We have focused on these components because they can include very important information for facial image analysis studies such as facial expression recognition. The segmentation success rate of the study is 98.01 according to average accuracy.
ISSN:2146-7390
2146-7390