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An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images.

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Title: An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images.
Authors: Park, Jeong-Seon1 jpark@chonnam.ac.kr, Seong-Whan Lee2 swlee@image.korea.ac.kr
Source: IEEE Transactions on Image Processing. Oct2008, Vol. 17 Issue 10, p1806-1816. 11p.
Subjects: Information storage & retrieval systems, Image databases, Human facial recognition software, Optical pattern recognition, Digital image processing, Image processing, Imaging systems, Feature extraction
Abstract: This paper proposes a face hallucination method for the reconstruction of high-resolution facial images from single-frame, low-resolution facial images. The proposed method has been derived from example-based hallucination methods and morphable face models. First, we propose a recursive error back-projection method to compensate for residual errors, and a region-based reconstruction method to preserve characteristics of local facial regions. Then, we define an extended morphable face model, in which an extended face is composed of the interpolated high-resolution face from a given low-resolution face, and its original high-resolution equivalent. Then, the extended face is separated into an extended shape and an extended texture. We performed various hallucination experiments using the MPI, XM2VTS, and KF databases, compared the reconstruction errors, structural similarity index, and recognition rates, and showed the effects of face detection errors and shape estimation errors. The encouraging results demonstrate that the proposed methods can improve the performance of face recognition systems. Especially the proposed method can enhance the resolution of single-frame, low-resolution facial images. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Image+Processing%22">IEEE Transactions on Image Processing</searchLink>. Oct2008, Vol. 17 Issue 10, p1806-1816. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+databases%22">Image databases</searchLink><br /><searchLink fieldCode="DE" term="%22Human+facial+recognition+software%22">Human facial recognition software</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+pattern+recognition%22">Optical pattern recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems%22">Imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
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  Data: This paper proposes a face hallucination method for the reconstruction of high-resolution facial images from single-frame, low-resolution facial images. The proposed method has been derived from example-based hallucination methods and morphable face models. First, we propose a recursive error back-projection method to compensate for residual errors, and a region-based reconstruction method to preserve characteristics of local facial regions. Then, we define an extended morphable face model, in which an extended face is composed of the interpolated high-resolution face from a given low-resolution face, and its original high-resolution equivalent. Then, the extended face is separated into an extended shape and an extended texture. We performed various hallucination experiments using the MPI, XM2VTS, and KF databases, compared the reconstruction errors, structural similarity index, and recognition rates, and showed the effects of face detection errors and shape estimation errors. The encouraging results demonstrate that the proposed methods can improve the performance of face recognition systems. Especially the proposed method can enhance the resolution of single-frame, low-resolution facial images. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1109/TIP.2008.2001394
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      – Code: eng
        Text: English
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        PageCount: 11
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      – SubjectFull: Information storage & retrieval systems
        Type: general
      – SubjectFull: Image databases
        Type: general
      – SubjectFull: Human facial recognition software
        Type: general
      – SubjectFull: Optical pattern recognition
        Type: general
      – SubjectFull: Digital image processing
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Imaging systems
        Type: general
      – SubjectFull: Feature extraction
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      – TitleFull: An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images.
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            NameFull: Park, Jeong-Seon
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            NameFull: Seong-Whan Lee
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            – D: 01
              M: 10
              Text: Oct2008
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              Y: 2008
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