Face and texture image analysis with quantized filter response statistics

Abstract

<div lang="en" class="abs"><p class="abs_header">Abstract</p><p>Image appearance descriptors are needed for different computer vision applications dealing with, for example, detection, recognition and classification of objects, textures, humans, etc. Typically, such descriptors should be discriminative to allow for making the distinction between different classes, yet still robust to intra-class variations due to imaging conditions, natural changes in appearance, noise, and other factors.</p><p>The purpose of this thesis is the development and analysis of photometric descriptors for the appearance of real life images. The two application areas included in this thesis are face recognition and texture classification.</p><p>To facilitate the development and analysis of descriptors, a general framework for image description using statistics of quantized filter bank responses modeling their joint distribution is introduced. Several texture and other image appearance descriptors, including the local binary pattern operator, can be presented using this model. This framework, within which the thesis is presented, enables experimental evaluation of the significance of each of the components of this three-part chain forming a descriptor from an input image.</p><p>The main contribution of this thesis is a face representation method using distributions of local binary patterns computed in local rectangular regions. An important factor of this contribution is to view feature extraction from a face image as a texture description problem. This representation is further developed into a more precise model by estimating local distributions based on kernel density estimation. Furthermore, a face recognition method tolerant to image blur using local phase quantization is presented.</p><p>The thesis presents three new approaches and extensions to texture analysis using quantized filter bank responses. The first two aim at increasing the robustness of the quantization process. The soft local binary pattern operator accomplishes this by making a soft quantization to several labels, whereas Bayesian local binary patterns make use of a prior distribution of labelings, and aim for the one maximizing the a posteriori probability. Third, a novel method for computing rotation invariant statistics from histograms of local binary pattern labels using the discrete Fourier transform is introduced.</p><p>All the presented methods have been experimentally validated using publicly available image datasets and the results of experiments are presented in the thesis. The face description approach proposed in this thesis has been validated in several external studies, and it has been utilized and further developed by several research groups working on face analysis.</p></div>