11 July 2016 Real-time robust face recognition using weight-incorporated LBP
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Proceedings Volume 10011, First International Workshop on Pattern Recognition; 1001109 (2016) https://doi.org/10.1117/12.2242813
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan
Abstract
In this paper, a new texture descriptor based on the extraction of image representation which is the selection of weights assigned to input and output of local binary patterns in determining the efficiency of each feature, called weight-incorporated local binary pattern (WiLBP), is developed for image representation. By using averaged gradients information, the principal components of a covariance matrix are derived to obtain an adjusted principal components of a maximum variance matrix, namely quantized eigen-analysis (QEA). The QEA matrix is a weight matrix used to adjust the contribution of comparisons of pixel intensities. To evaluate the performance of the WiLBP, a series of experiments was tested on some popular face databases. The misclassification error obtained by the QEA across most trials is lower than that of the PCA. The experimental results also show that the WiLBP is a fast and robust method in individual recognition and gender classification applications.
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Ren-He Jeng, Ren-He Jeng, Wen-Shiung Chen, Wen-Shiung Chen, Lili Hsieh, Lili Hsieh, } "Real-time robust face recognition using weight-incorporated LBP", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 1001109 (11 July 2016); doi: 10.1117/12.2242813; https://doi.org/10.1117/12.2242813
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