Optimization of process parameters for scanning human face using hand-held scanner

Abstract

Abstract Three-dimensional surface scanning of several anatomical areas or human body has gained popularity in current decades due to pre-surgical planning and improved workflow of patient diagnosis and treatment Living surfaces, such as the human face, have various degrees of surface complexity to account for, as well as a range of process parameters to consider. In the proposed work, the face of a person was scanned in various combinations of input parameters using a handheld laser scanner, SENSE 3D (3D system, Rock Hill, SC/USA). Scanner to surface distance, angular orientation, and illumination intensity are considered significant input parameters while using laser scanners for 3D facial data. A number of twenty experimental runs and input parameter combination were suggested by face centered central composite design. The human face has been scanned on these twenty runs to retrieve 3D CAD model and FID score of each model has been completed to investigate the quality/accuracy of the captured data. A model has been trained among input and output using a neural network and further, it is optimized using a genetic algorithm to maximize accuracy The minimum, FID score achieved 270.24, obtained with a scanning distance of 22 inches, the angular orientation of 67.5 degrees, and ambient lightning condition of 16 watt/meter square in twenty experimental runs. The accuracy is maximized by minimizing the FID score utilizing a heuristic GA-ANN technique having 28 inches as scanning distance, 48.041 degrees as angular orientation, and 18 watt/meter square as the ambient lighting condition.