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

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:10577149
DOI:10.1109/TIP.2008.2001394