Abstract
This paper proposes a new method of enhancing the resolution of facial image from a low-resolution facial image using a recursive error back-projection of example-based 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 obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. In addition to, a recursive error back-projection is applied to improve the accuracy of resolution enhancement. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at visual surveillance systems.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition |
| Pages | 831-836 |
| Number of pages | 6 |
| Publication status | Published - 2004 Sep 24 |
| Event | Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004 - Seoul, Korea, Republic of Duration: 2004 May 17 → 2004 May 19 |
Other
| Other | Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 04/5/17 → 04/5/19 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Resolution enhancement of facial image using an error back-projection of example-based learning. / Park, Jeong Seon; Lee, Seong Whan.
Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition. 2004. p. 831-836.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Resolution enhancement of facial image using an error back-projection of example-based learning
AU - Park, Jeong Seon
AU - Lee, Seong Whan
PY - 2004/9/24
Y1 - 2004/9/24
N2 - This paper proposes a new method of enhancing the resolution of facial image from a low-resolution facial image using a recursive error back-projection of example-based 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 obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. In addition to, a recursive error back-projection is applied to improve the accuracy of resolution enhancement. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at visual surveillance systems.
AB - This paper proposes a new method of enhancing the resolution of facial image from a low-resolution facial image using a recursive error back-projection of example-based 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 obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. In addition to, a recursive error back-projection is applied to improve the accuracy of resolution enhancement. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at visual surveillance systems.
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M3 - Conference contribution
AN - SCOPUS:4544235729
SN - 0769521223
SP - 831
EP - 836
BT - Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition
ER -