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This paper describes an image segmentation and normalization technique using 3D point distribution model and its counterpart in 2D space. This segmentation is efficient to work for holistic image recognition algorithm. The results have been tested with face recognition application using Cohn Kanade facial expressions database (CKFED). The approach follows by fitting a model to face image and registering it to a standard template. The models consist of distribution of points in 2D and 3D. We extract a set of feature vectors from normalized images using principal components analysis and using them for a binary decision tree for classification. A promising recognition rate of up to 98.75% has been achieved using 3D model and 92.93% using 2D model emphasizing the goodness of our normalization. The experiments have been performed on more than 3500 face images of the database. This algorithm is capable to work in real time in the presence of facial expressions.