See a list of publications below or visit the Photonics academic staff page and click on a particular  member of staff to access their personal web page, which includes a list of their own publications.

Citation

BibTex format

@article{Kumar:2018:10.1016/j.jvcir.2018.04.013,
author = {Kumar, S and Bhuyan, MK and Lovell, BC and Iwahori, Y},
doi = {10.1016/j.jvcir.2018.04.013},
journal = {Journal of Visual Communication and Image Representation},
pages = {171--181},
title = {Hierarchical uncorrelated multiview discriminant locality preserving projection for multiview facial expression recognition},
url = {http://dx.doi.org/10.1016/j.jvcir.2018.04.013},
volume = {54},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Existing multi-view facial expression recognition algorithms are not fully capable of finding discriminative directions if the data exhibits multi-modal characteristics. This research moves toward addressing this issue in the context of multi-view facial expression recognition. For multi-modal data, local preserving projection (LPP) or local Fisher discriminant analysis (LFDA)-based approach is quite appropriate to find a discriminative space. Also, the classification performance can be enhanced by imposing uncorrelated constraint onto the discriminative space. So for multi-view (multi-modal) data, we proposed an uncorrelated multi-view discriminant locality preserving projection (UMvDLPP)-based approach to find an uncorrelated common discriminative space. Additionally, the proposed UMvDLPP is implemented in a hierarchical fashion (H-UMvDLPP) to obtain an optimal performance. Extensive experiments on BU3DFE dataset show that UMvDLPP performs slightly better than the existing methods. However, an improvement of approximately 3% as compared to the existing state-of-the-art multi-view learning-based approaches is achieved by our H-UMvDLPP. This improvement is due to the fact that the proposed method enhances the discrimination between the classes more effectively, and classifies expressions category-wise followed by classification of the basic expressions embedded in each of the subcategories (hierarchical approach).
AU - Kumar,S
AU - Bhuyan,MK
AU - Lovell,BC
AU - Iwahori,Y
DO - 10.1016/j.jvcir.2018.04.013
EP - 181
PY - 2018///
SN - 1047-3203
SP - 171
TI - Hierarchical uncorrelated multiview discriminant locality preserving projection for multiview facial expression recognition
T2 - Journal of Visual Communication and Image Representation
UR - http://dx.doi.org/10.1016/j.jvcir.2018.04.013
VL - 54
ER -