Face segmentation in thumbnail images by data-adaptive convolutional segmentation networks

Abstract

In this study we address the problem of face segmentation in thumbnail images. While there have been several approaches for face detection, none performs detection in such low resolution and segmentation with pixel accuracy. In this paper, we propose convolutional segmentation networks (CSNs) that can be trained to learn segmentation of human faces. Unlike the deep classifiers such as Convolutional Neural Network (CNNs), CSNs have the unique design solely for segmentation with minimal complexity. Furthermore, we propose a self-data organization (SDO) in order to create 'expert' CSNs each of which is specialized over a set of images with certain face characteristics. SDO is integrated with CSN training in an interleaved manner and it is the key for the learning with simple and compact networks rather than the deep ones. This is especially a desired property for the limited face datasets with challenging face variations and complexities. Evaluations on the benchmark dataset show that CSNs can achieve an elegant segmentation accuracy despite the limited training data size, thumbnail resolution and highly complex face modalities.