University of Illinois Urbana-Champaign

Very Low-Quality Recognition Using Conventional Neural Network: With an Application to Face Identification

Cheng, Bowen

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https://hdl.handle.net/2142/97846

Description

Title
Very Low-Quality Recognition Using Conventional Neural Network: With an Application to Face Identification
Author(s)
Cheng, Bowen
Contributor(s)
Huang, Thomas S.
Issue Date
2017-05
Keyword(s)
  • Convolutional neural network
  • Computer vision
  • Classification
  • Recognition
  • Face recognition
Abstract
Visual recognition from very low-quality images is an extremely challenging task with great practical values, due to the ubiquitous existence of quality distortions during image acquisition, transmission, or storage. While deep networks have been extensively applied to low-quality image restoration and high-quality image recognition tasks respectively, less has been done on the important problem of recognition from very low-quality images. I propose a degradation-robust pre-training method to jointly tune reconstruction and classification with comprehensive analysis on improving deep learning models along this direction. This jointly tuning leverages the power of pre-training similar to that of transfer learning and generalizes conventional unsupervised pre-training and data augmentation methods. I did extensive experiments on a number of diverse real-world datasets to validate the effectiveness of the proposed method and applied this method on face identification tasks.
Type of Resource
text
Language
en
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http://hdl.handle.net/2142/97846

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