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 |
|---|---|
| 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 |
|---|---|
| Country | United States |
| City | Berkeley |
| Period | 03/11/2 → 03/11/8 |
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Keywords
- Face recognition
- Face reconstruction
- Low-resolution
- Morphable face model
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications
Cite this
Resolution enhancement of facial image based on topdown learning. / Park, Jeong Seon; Lee, Seong Whan.
1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003. Association for Computing Machinery, Inc, 2003. p. 59-64.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Resolution enhancement of facial image based on topdown learning
AU - Park, Jeong Seon
AU - Lee, Seong Whan
PY - 2003/11/2
Y1 - 2003/11/2
N2 - 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.
AB - 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.
KW - Face recognition
KW - Face reconstruction
KW - Low-resolution
KW - Morphable face model
UR - http://www.scopus.com/inward/record.url?scp=84968768275&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84968768275&partnerID=8YFLogxK
U2 - 10.1145/982452.982460
DO - 10.1145/982452.982460
M3 - Conference contribution
AN - SCOPUS:84968768275
SP - 59
EP - 64
BT - 1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003
PB - Association for Computing Machinery, Inc
ER -