Abstract
This paper proposes a new method of synthesizing a highresolution facial image from a low-resolution facial image based on top-down learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be synthesized by using the optimal coefficients for linear combination of the high-resolution prototypes. The encouraging results of the proposed method show that our method can be used to increase the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at surveillance systems.
Original language | English |
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Title of host publication | 1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003 |
Publisher | Association for Computing Machinery, Inc |
Pages | 59-64 |
Number of pages | 6 |
ISBN (Electronic) | 158113780X, 9781581137804 |
DOIs | |
Publication status | Published - 2003 Nov 2 |
Event | 1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003 - Berkeley, United States Duration: 2003 Nov 2 → 2003 Nov 8 |
Other
Other | 1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003 |
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Country | United States |
City | Berkeley |
Period | 03/11/2 → 03/11/8 |
Keywords
- Face recognition
- Face reconstruction
- Low-resolution
- Morphable face model
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications