Automatic Analysis of Smoothing Techniques by Simulation Model Based Real-time System for Processing 3d Human Faces

The pivotal research work that has been carried out and described in this literature acknowledges the importance of various smoothing techniques for processing 3D human faces from 2.5D range face images. The smoothing techniques have been developed and implemented using MATLAB-Simulink for real time processing in embedded system. In addition, the significance of smoothed 2.5D range image over original face range image has been discovered as well as its time complexity has also been reported with array of experiments. The variations in time complexities are also accomplished using different optimization levels and execution modes. A set of filtering techniques such as, Max filter, Min filter, Median filter, Mean filter, Mid-point filter and Gaussian filter, have been designed and illustrated using Simulink model. The model takes depth face image (i.e. the range face image) as input in real time and presents the improvement over original face images. In the design flow, the performance of every block has also been characterized by range face images from Frav3D, GavabDB, and Bosphorus databases. In the experimental section of this research article, an array of performance analysis for these smoothing techniques with variation of frameworks is explained.