Loading [MathJax]/extensions/MathMenu.js
Classifying Facial Regions for Face Hallucination | IEEE Journals & Magazine | IEEE Xplore

Classifying Facial Regions for Face Hallucination


Abstract:

Recently, convolutional neural networks (CNNs) have dominated the face hallucination task due to their powerful feature representation capability. However, most of them s...Show More

Abstract:

Recently, convolutional neural networks (CNNs) have dominated the face hallucination task due to their powerful feature representation capability. However, most of them simply use the same weights to treat different facial regions without considering the reconstruction difficulty of different facial regions, resulting in the component regions (e.g., eyes, nose, mouth) of the reconstructed faces tending to be blurred. In this paper, we propose a novel facial region classification network (FRCN) to address this problem. The proposed method first divides the input low-resolution (LR) facial image into several patch blocks, then classifies them into three categories according to their reconstruction difficulty, and finally inputs the three types of patch blocks into three networks with different weights for reconstruction and combining, thereby recovering high-quality high-resolution (HR) facial image. Experimental results show that FRCN can remarkably improve face reconstruction's performance.
Published in: IEEE Signal Processing Letters ( Volume: 29)
Page(s): 2392 - 2396
Date of Publication: 11 November 2022

ISSN Information:

Funding Agency:


References

References is not available for this document.