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.

Date Published: 11 July 2016
PDF: 5 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 1001109 (11 July 2016); doi: 10.1117/12.2242813

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)