Sensors | |
Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap | |
Xiaoming Zhao1  | |
[1] Department of Computer Science, Taizhou University, Taizhou 317000, China | |
关键词: kernel; isometric mapping; dimensionality reduction; local binary patterns; facial expression recognition; | |
DOI : 10.3390/s111009573 | |
来源: mdpi | |
【 摘 要 】
Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases,
【 授权许可】
CC BY
© 2011 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202003190047804ZK.pdf | 587KB | download |