Özet:

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.

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