Three dimensional face recognition using two dimensional principal component analysis

This dissertation describes a face recognition system that overcomes the problem of changes in gesture and mimics in three-dimensional (3D) range images. Here, we propose a local variation detection and restoration method based on the two-dimensional (2D) principal component analysis (PCA). The depth map of a 3D facial image is first thresholded to discard the background information. The detected face shape is normalized to a standard image size of 100x100 pixels and the forefront nose point is selected to be the image center. Facial depth-values are scaled between 0 and 255 for translation and scaling-invariant identification. The preprocessed face image is smoothed to minimize the local variations. The 2DPCA is applied to the resultant range data and the corresponding principal- (or eigen-) images are used as the characteristic feature vectors of the subject to find his/her identity in the database of pre-recorded faces.; The system's performance is tested against the GavabDB and Notre Dame University facial databases. Experimental results show that the proposed method is able to identify subjects with different gesture and mimics in the presence of noise in their 3D facial images.