Extracting a Good Quality Frontal Face Image from a Low-Resolution Video Sequence

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20 Citations (Scopus)

Abstract

Feeding low-resolution and low-quality images, from inexpensive surveillance cameras, to systems like, e.g., face recognition, produces erroneous and unstable results. Therefore, there is a need for a mechanism to bridge the gap between on one hand low-resolution and low-quality images and on the other hand facial analysis systems. The proposed system in this paper deals with exactly this problem. Our approach is to apply a reconstruction-based super-resolution algorithm. Such an algorithm, however, has two main problems: first, it requires relatively similar images with not too much noise and second is that its improvement factor is limited by a factor close to two. To deal with the first problem we introduce a three-step approach, which produces a face-log containing images of similar frontal faces of the highest possible quality. To deal with the second problem, limited improvement factor, we use a learning-based super-resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. This results in an improvement factor of four for the entire system. The proposed system has been tested on 122 low-resolution sequences from two different databases. The experimental results show that the proposed system can indeed produce a high-resolution and good quality frontal face image from low-resolution video sequences.
Original languageEnglish
JournalI E E E Transactions on Circuits and Systems for Video Technology
Volume21
Issue number10
Pages (from-to)1353 - 1362
ISSN1051-8215
DOIs
Publication statusPublished - 2011

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Image quality
Face recognition
Cameras

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@article{7d06bcbfda80470b8aae69086905fb31,
title = "Extracting a Good Quality Frontal Face Image from a Low-Resolution Video Sequence",
abstract = "Feeding low-resolution and low-quality images, from inexpensive surveillance cameras, to systems like, e.g., face recognition, produces erroneous and unstable results. Therefore, there is a need for a mechanism to bridge the gap between on one hand low-resolution and low-quality images and on the other hand facial analysis systems. The proposed system in this paper deals with exactly this problem. Our approach is to apply a reconstruction-based super-resolution algorithm. Such an algorithm, however, has two main problems: first, it requires relatively similar images with not too much noise and second is that its improvement factor is limited by a factor close to two. To deal with the first problem we introduce a three-step approach, which produces a face-log containing images of similar frontal faces of the highest possible quality. To deal with the second problem, limited improvement factor, we use a learning-based super-resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. This results in an improvement factor of four for the entire system. The proposed system has been tested on 122 low-resolution sequences from two different databases. The experimental results show that the proposed system can indeed produce a high-resolution and good quality frontal face image from low-resolution video sequences.",
author = "Kamal Nasrollahi and Moeslund, {Thomas B.}",
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doi = "10.1109/TCSVT.2011.2162267",
language = "English",
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pages = "1353 -- 1362",
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Extracting a Good Quality Frontal Face Image from a Low-Resolution Video Sequence. / Nasrollahi, Kamal; Moeslund, Thomas B.

In: I E E E Transactions on Circuits and Systems for Video Technology, Vol. 21, No. 10, 2011, p. 1353 - 1362.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Extracting a Good Quality Frontal Face Image from a Low-Resolution Video Sequence

AU - Nasrollahi, Kamal

AU - Moeslund, Thomas B.

PY - 2011

Y1 - 2011

N2 - Feeding low-resolution and low-quality images, from inexpensive surveillance cameras, to systems like, e.g., face recognition, produces erroneous and unstable results. Therefore, there is a need for a mechanism to bridge the gap between on one hand low-resolution and low-quality images and on the other hand facial analysis systems. The proposed system in this paper deals with exactly this problem. Our approach is to apply a reconstruction-based super-resolution algorithm. Such an algorithm, however, has two main problems: first, it requires relatively similar images with not too much noise and second is that its improvement factor is limited by a factor close to two. To deal with the first problem we introduce a three-step approach, which produces a face-log containing images of similar frontal faces of the highest possible quality. To deal with the second problem, limited improvement factor, we use a learning-based super-resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. This results in an improvement factor of four for the entire system. The proposed system has been tested on 122 low-resolution sequences from two different databases. The experimental results show that the proposed system can indeed produce a high-resolution and good quality frontal face image from low-resolution video sequences.

AB - Feeding low-resolution and low-quality images, from inexpensive surveillance cameras, to systems like, e.g., face recognition, produces erroneous and unstable results. Therefore, there is a need for a mechanism to bridge the gap between on one hand low-resolution and low-quality images and on the other hand facial analysis systems. The proposed system in this paper deals with exactly this problem. Our approach is to apply a reconstruction-based super-resolution algorithm. Such an algorithm, however, has two main problems: first, it requires relatively similar images with not too much noise and second is that its improvement factor is limited by a factor close to two. To deal with the first problem we introduce a three-step approach, which produces a face-log containing images of similar frontal faces of the highest possible quality. To deal with the second problem, limited improvement factor, we use a learning-based super-resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. This results in an improvement factor of four for the entire system. The proposed system has been tested on 122 low-resolution sequences from two different databases. The experimental results show that the proposed system can indeed produce a high-resolution and good quality frontal face image from low-resolution video sequences.

U2 - 10.1109/TCSVT.2011.2162267

DO - 10.1109/TCSVT.2011.2162267

M3 - Journal article

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SP - 1353

EP - 1362

JO - I E E E Transactions on Circuits and Systems for Video Technology

JF - I E E E Transactions on Circuits and Systems for Video Technology

SN - 1051-8215

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