Abstract
Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.
| Original language | English |
|---|---|
| Title of host publication | IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings |
| Publisher | IEEE Computer Society |
| ISBN (Print) | 9781479928439 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore Duration: 2014 Apr 21 → 2014 Apr 24 |
Other
| Other | 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 14/4/21 → 14/4/24 |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
Cite this
Enhanced discriminant linear regression classification for face recognition. / Qu, Xiaochao; Kim, Hyong Joong.
IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. 6827696.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Enhanced discriminant linear regression classification for face recognition
AU - Qu, Xiaochao
AU - Kim, Hyong Joong
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.
AB - Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.
UR - http://www.scopus.com/inward/record.url?scp=84903727721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903727721&partnerID=8YFLogxK
U2 - 10.1109/ISSNIP.2014.6827696
DO - 10.1109/ISSNIP.2014.6827696
M3 - Conference contribution
AN - SCOPUS:84903727721
SN - 9781479928439
BT - IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
PB - IEEE Computer Society
ER -