Local saliency-inspired binary patterns for automatic recognition of multi-view facial expression
Document Type
Conference Article
Publication Title
Proceedings - International Conference on Image Processing, ICIP
Abstract
In this paper, we propose a novel scheme for automatic recognition of facial expressions captured from both fronto-parallel and non-fronto-parallel cameras i.e., multi-view facial expressions (MVFE). The proposed scheme introduce a Local Saliency-inspired Binary Pattern (LSiBP) feature to recognize MVFE. First view-specific approximated saliency likelihood map (ASLM) is derived during training of our model. ASLM is determined from the 2D structure tensor representation of faces. The distribution of saliency likelihoods of pixels along with pixel intensities are analyzed in extracting LSiBP features from a face. Such LSiBP features are utilized for training and testing of view-specific SVM classifiers. Extensive experiments are performed on datasets of both posed and unconstrained spontaneous expressions of MVFE. Our scheme outperforms state-of-the-arts by at least 1% to recognize MVFE.
First Page
624
Last Page
628
DOI
10.1109/ICIP.2016.7532432
Publication Date
8-3-2016
Recommended Citation
Santra, Bikash and Mukherjee, Dipti Prasad, "Local saliency-inspired binary patterns for automatic recognition of multi-view facial expression" (2016). Conference Articles. 742.
https://digitalcommons.isical.ac.in/conf-articles/742
