27 September 2017 Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition
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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.
© 2017 SPIE and IS&T
Maryam Sultana, Muhammad Naeem Ali Bhatti, Sajid Javed, Soon-Ki Jung, "Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition," Journal of Electronic Imaging 26(5), 053017 (27 September 2017). https://doi.org/10.1117/1.JEI.26.5.053017 . Submission: Received: 9 May 2017; Accepted: 30 August 2017
Received: 9 May 2017; Accepted: 30 August 2017; Published: 27 September 2017
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