Real-time Face Segmentation Using Progressive Growing of Convolutional Neural Networks


JIETA, Vol.3 no.1, pp.145-149, 2020

DOI: soon

Real-time Face Segmentation Using Progressive Growing of Convolutional Neural Networks

Myoung-Kyu Sohn 1,*), Sang-Heon Lee 1), Hyunduk Kim 1)
1) Division of Automotive Technology, DGIST, Daegu, Republic of Korea

 

Abstract: Semantic segmentation on an image is increasingly required in more and more fields such as scene understanding, inference of object relationships for autonomous driving and object extraction of interest. This technique gives the ability to segment different parts and objects from an image. Recently, there have also been many improvements in segmentation based on deep learning techniques. In particular, SegNet has improved the noisy image from the results of pixel-wise labelling. In this paper, a deep learning method for the segmentation of each area in an image is proposed. We introduce a progressive growing of convolutional neural networks that can learn quickly and increase the recognition rate using various resolutions. Then we compare our results to the learning methods using conventional convolutional neural networks architectur

 

Keyword: semantic segmentation, deep learning, convolutional neural networks, neural networks

 

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