Discriminant Common Vectors (DCV) is proposed to solve small sample size problem. Face recognition encounters this dilemma where number of training samples is always smaller than the data dimension. In literature, it is shown that DCV is efficient in face recognition. In this paper, DCV is enhanced for further boosting its discriminating power. This modified version is namely Discriminative Discriminant Common Vectors (DDCV). In this technique, a local Laplacian matrix of face data is computed. This matrix is used to derive a regularization model for computing discriminative class common vectors. Experimental results demonstrate that DDCV illustrates its effectiveness on face verification, especially on facial images with significant intra class variations.
|Title of host publication||2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2014 Jul 30|
|Event||2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - Kuala Lumpur, Malaysia|
Duration: 2014 Jun 3 → 2014 Jun 5
|Name||2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings|
|Other||2014 International Conference on Computer and Information Sciences, ICCOINS 2014|
|Period||14/6/3 → 14/6/5|
Bibliographical noteFunding Information:
*A. L. acknowledges the support by the Ministry of Science and Technology of Republic of Slovenia (Project J2-0414 and SI-CZ Intergovernmental S & T Cooperation Programme). H. B. was supported by a grant from the Fonds zur Forderung der wissenschaftlichen Forschung (No. S7002MAT and P13981INF). We thank Jasna Maver for performing some of the experiments.
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Information Systems
- Environmental Engineering
- Renewable Energy, Sustainability and the Environment
- Computer Science Applications