Multi-Resolution Face Recognition: The Behaviors of Local Binary Pattern at Different Frequency Bands
Nova Hadi Lestriandoko, Spreeuwers, Luuk, Raymond N. J. Veldhuis, Callebaut, Gilles, Verniers, Kevin, Cox, Bert · University of Twente Research Information · 2019
This paper presents an analysis of the recognition performance of LBP at different frequency bands to exploit their discriminative information. The work presented in this paper is part of an investigation about which aspects of a face contribute to automated face recognition. Multi-resolution analysis, by means of wavelet transform, is commonly used to explore the features of an image. The each step of wavelet transform decomposes an image recursively into four frequency bands: approximation, horizontal, vertical, and diagonal band. The approximation band is a down sampled version of the original image. Whereas, the other bands are detail bands that contain detail information of the image in horizontal, vertical, and diagonal directions. The noise is more dominant in these bands. The information contained in the detail bands is more related to high frequency-components and local structures such as edges. In order to analyze the impact of the various bands, we performed classification experiments on them. For the A-bands, that contain the global information of the facial image, we used PCA/LDA classifiers. For the detail bands, that contain local structures, we used LBP.