Biometric identification has received much attention due to the increasing demand on reliably characterizing individuals. Of all the biometric features, face recognition remains one of the most active research issue because of its advantages, such as most accessible and easier to collect. However, since the human face is a 3D deformable object with textures in nature, traditional intensity face recognition must be influenced by illuminations, poses and expressions, which results in the loss of some discriminative information. To solve these problems, 3D face modality has attracted more and more attention in recent years. 3D face recognition and categorization tries to make it possible that the computers can make the recognition and categorization decisions similar to humans, which can be achieved by accurate analysis of the 3D facial data collected from some 3D scanners. In this thesis, we have a comprehensive study on the features which can efficiently and robustly characterize the 3D facial data based on the Learned Visual Codebook. The main contributions are as follows: 1.We accurately locate the nose area in 3D facial data, based on which we also achieve face registration between different individuals. 2.We propose Learned Visual Codebook (LVC) features to efficiently represent the 3D facial data, which can be successfully applied into the 3D face recognition system. Some 3D facial textons are first learned using clustering. Then these learned textons are adopted as the bases of the histogram. And a histogram vector can be obtained by mapping the 3D face into these learned textons, which is the final representation of the original 3D facial data. Experimental results illustrate that LVC can combine the generality, efficiency and robustness together. 3.We also modify the LVC algorithm in each procedure of the recognition framework, including filter responses, clustering and matching distances selection, which significantly improve the recognition performance in both the FRGC2.0 and CASIA 3D Face Database. 4.We propose the Robust Local Log-Gabor Histogram (RLLGH) features to overcome the problems encountered in un-controlled environments, which achieve promising performance for both the expression situations in CASIA 3D Face Database and the large expression situations in FRGC2.0 3D Face Database. 5.We introduce the fuzzy 3D face categorization based on the LVC features. First the eastern codes and western codes are learned from the predefined traini...
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