3D Face Recognition Based On 2D-Gabor Feature Extraction

Illumination,gesture and expression are three core problems of face recognition that wait to be solved urgently these days.2-D face recognition algorithms can be quite accurate in extracting face features and obtaining preferable recognition results when the faces are those frontal faces under normal illuminations and with neutral expressions.However,in respect that 2-D face recognition algorithms do not take 3-D shape information into account,many algorithms have too bad robustness towards illumination,gesture and expression.In contrast,as 3-D model gives more detailed shape information of face and is quite different from the 2-D image which could only give the projection of the face into a surface in one direction,3-D model could engender those algorithms that are quite robust towards illumination,gesture and expression.Followed by 3-D model reconstruction,a 3-D face recognition algorithm based on 2-D Gabor feature extraction is brought forward.This algorithm is based on accurate 2-D Gabor features which could stably describe features on human face.The 3-D face recognition is performed by three steps:3-D face model reconstruction,pattern matching of reconstructed 3-D face model and Fisher's Linear Discriminant Analysis of matched 3-D face model.The experiments developed on ORL(Olivetti Research Laboratory) and UMIST(University of Manchester Institute of Science and Technology) databases show that the proposed method has excellent performances.An illumination space is defined and applied to effectively reduce influences of illumination and gesture by designing a face appearance model and applying illumination compensation as well as optimization of gesture recognition.A 3-D grid model is introduced to construct the geometric information of the whole face.The whole process of expression recognition is put forward by model matching, creating the deformable model graph,wavelet analysis and data processing in the phase of recognition as well as verification.