FPL-An End-to-End Face Parts Labeling Framework

Abstract

Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method FPL which divides a given image into its constitutes parts is proposed in this paper. In most of the previously proposed methods this division is based on three or some time four classes. In the proposed work a given face image is divided into six classes (skin, hair, back, eyes, nose and mouth). A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. Testing phase is performed with two semantic segmentation methods i.e., pixel and super-pixel based segmentation. In pixel based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixels only – as a result same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68% and 93.45% respectively.