Loading [MathJax]/extensions/MathMenu.js
A Noise Robust Face Hallucination Framework Via Cascaded Model of Deep Convolutional Networks and Manifold Learning | IEEE Conference Publication | IEEE Xplore

A Noise Robust Face Hallucination Framework Via Cascaded Model of Deep Convolutional Networks and Manifold Learning


Abstract:

Face hallucination technique generates high-resolution clean faces from low-resolution ones. Traditional technique generates facial features by incorporating manifold str...Show More

Abstract:

Face hallucination technique generates high-resolution clean faces from low-resolution ones. Traditional technique generates facial features by incorporating manifold structure into patch representation. In recent years, deep learning techniques have achieved great success on the topic. These deep learning based methods can well maintain the middle and low frequency information. However, they still cannot well recover the high-frequency facial features, especially when the input is contaminated by noise. To address this problem, we propose a novel noise robust face hallucination framework via cascaded model of deep convolutional networks and manifold learning. In general, we utilize convolutional network to remove the noise and generate medium and low frequency facial information; then, we further utilize another convolutional network to compensate the lost high frequency with the help of personalized manifold learning method. Experimental results on public dataset show the superiority of our method compared with state-of-the-art methods.
Date of Conference: 23-27 July 2018
Date Added to IEEE Xplore: 11 October 2018
ISBN Information:

ISSN Information:

Conference Location: San Diego, CA, USA

References

References is not available for this document.