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

Authors:
Jeong-Seon Park;Seong-Whan Lee
Affiliations:
Dept. of Multimedia, Chonnam Nat. Univ., Yosu;-
Venue:
IEEE Transactions on Image Processing
Year:
2008

Citing 0
Cited 12

Hallucinating Faces: Global Linear Modal Based Super-Resolution and Position Based Residue Compensation

ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Cooperative face hallucination using multiple references

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Hallucinating face by position-patch

Pattern Recognition
A discriminated correlation classifier for face recognition

Proceedings of the 2010 ACM Symposium on Applied Computing
Face hallucination with shape parameters projection constraint

Proceedings of the international conference on Multimedia
Global face super resolution and contour region constraints

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Sparse representation and position prior based face hallucination upon classified over-complete dictionaries

Signal Processing
A survey of face hallucination

CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Face hallucination based on sparse local-pixel structure

Pattern Recognition
A Comprehensive Survey to Face Hallucination

International Journal of Computer Vision
Super-resolution for facial image using multilateral affinity function

Neurocomputing
Low-resolution face recognition: a review

The Visual Computer: International Journal of Computer Graphics

Quantified Score

Hi-index	0.01

Visualization

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.