FACE HALLUCINATION BASED ON KEY PARTS ENHANCEMENT

被引：0
作者：
Li, Ke [1 ]
Bare, Bahetiyaer [1 ]
Yan, Bo [1 ]
Feng, Bailan [2 ]
Yao, Chunfeng [2 ]
机构：
[1] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China
[2] Huawei Technol Co Ltd, 2012Labs, Noahs Ark Lab, Beijing, Peoples R China
来源：
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年
关键词：
Face hallucination; Convolutional neural networks; Super-resolution;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Face hallucination aims to generate a high resolution face from a low resolution one. Generic super resolution methods can not solve this problem well, because human face has a strong structure. With the rapid development of the deep learning technique, some convolutional neural networks (CNNs) models for face hallucination emerged and achieved state-of-the-art performance. In this paper, we proposed a five-branch network based on five key parts of human face. Each branch of this network aims to generate a high resolution key part. The final high resolution face is the combination of the five branches' output. In addition, we designed a gated enhance unit (GEU) and cascade it to form our network architecture. Experimental results confirm that our method can generate pleasing high resolution faces.
引用
收藏
页码：1378 / 1382
页数：5
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