Face Sketch Synthesis using Conditional Adversarial Networks

被引：0

作者：

Philip, Chikontwe ^{[1
]}

Jong, Lee Hyo ^{[2
]}

机构：

[1] Chonbuk Natl Univ, Div Comp Sci & Engn, Jeonju 561756, South Korea

[2] Chonbuk Natl Univ, Div Comp Sci & Engn, Ctr Adv Image & Informat Technol, Jeonju 561756, South Korea

来源：

2017 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC) | 2017年

基金：

新加坡国家研究基金会;

关键词：

adversarial networks; convolutional neural networks; conditional generative adversarial networks; deep learning; face sketch sythesis; style transfer; RECOGNITION;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we explore the use of recent conditional generative adversarial network framework for image to image translation applied to the domain of heterogeneous face sketch synthesis. Since the inception of the adversarial framework in 2014, great success has been noted with several variants till date. Further, we introduce a new dataset for composite sketch images. In particular we explore sketch to digital photo, digital photo to sketch as well as composite sketch to digital image translations. The results indicate the great potential of the adversarial frameworks for face sketch synthesis.

引用

页码：373 / 378

页数：6

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