Face Recognition System Based on Spatial Constellation Model and Support Vector Machine
| Autor: | Wei-ming Lan, 藍偉銘 |
|---|---|
| Rok vydání: | 2010 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 98 This research presents a comprehensive recognition system based on spatial constellation model using advanced multi-resolution block local binary patterns. We perform object-oriented design code to build our system and do the experiments of face recognition using ORL and Extended Yale B face database. We usually do dimension reduction and get features from facial images based on model-based method for the conventional face recognition. This kind of methods is holistic when they deal with the image data, for example, PCA (Principle Component Analysis), LDA (Linear Discriminent Analysis) and so on. Researcher use this kind of methods is because they are good methods to do dimension reduction in statistics. They find the relation of input data which are good for we to represent the data. However, these holistic model methods are easily affected by the variance of images we get such like illumination change and insufficient normalization. In the past few years, some descriptors like Gabor Filter and local binary pattern used in pattern recognition are noticed and be used in face recognition or other biometrics. Because these descriptors get the features from images are local, that is why we use it. We can avoid insufficient normalization problem that usually happen in holistic method. Our research is based on LBP (local binary pattern) and we use Gaussian Mixture Model, it’s a good method to model the phenomena in the nature world, to model the distribution of the characteristic points in spatial domain. Using parameters of GMM we get, we can construct good features which can robust against the distortion of input image like variation of shift, scale, and rotation. We use multi-class II support vector machines to train our data in the database, and we make the final decision by counting the votes of each support vector machine. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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