Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/62538
Title: Visual recognition by subspace approaches on LBP features
Authors: Ren, Jianfeng
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2015
Source: Ren, J. (2015). Visual recognition by subspace approaches on LBP features. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Traditionally, subspace approaches are applied on the holistic features. Recently, local binary pattern (LBP) has become popular because it is robust to illumination variations and alignment error. In this thesis, we exploit the advantages of both. Firstly, we propose a fast and accurate subspace face/eye detector and build a complete and fully automated face verification system on mobile devices. Secondly, to improve the robustness to image noise, we propose a noise-resistant LBP (NRLBP) with an embedded error-correction mechanism. Thirdly, we derive a data-driven LBP through optimizing the LBP structure directly using Maximal-Conditional-Mutual-Information scheme, towards the objective of reducing the LBP feature dimensionality and deriving discriminative LBP structures. Fourthly, to better remove unreliable dimensions of LBP histogram, we propose a patch-dependent/independent learning-based LBP. Lastly, to handle non-Gaussian distribution of LBP features, we propose a Chi-squared transformation that enhances the performance gain of subspace approaches on LBP features.
URI: http://hdl.handle.net/10356/62538
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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