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Journal of Electronic Imaging • new

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition
Author(s): Maryam Sultana; Muhammad Naeem Ali Bhatti; Sajid Javed; Soon-Ki Jung
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Paper Abstract

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

Paper Details

Date Published: 27 September 2017
PDF: 17 pages
J. Electron. Imag. 26(5) 053017 doi: 10.1117/1.JEI.26.5.053017
Published in: Journal of Electronic Imaging Volume 26, Issue 5
Show Author Affiliations
Maryam Sultana, Kyungpook National Univ. (Republic of Korea)
Muhammad Naeem Ali Bhatti, Quaid-i-Azam Univ. (Pakistan)
Sajid Javed, Kyungpook National Univ. (Republic of Korea)
Soon-Ki Jung, Kyungpook National Univ. (Republic of Korea)

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