Abstract: The printing quality of 3D portrait in 3D printing photographic house, depend on the 3D
scan of the three dimensional human body model identification. However, the traditional 3D body
scanner is expensive, complicated manipulation and other reasons, 3D portraits of high printing costs,
time-consuming and less accurate printing. In view of these shortcomings a method is proposed based
on the deep scanner reconstruct the high degree of recognition three-dimensional human body model.
The method combines with the advantages of three groups of different types of the depth scanner,
collaboration, respectively to obtain high precision characters facial features and hair detail point
cloud data, upper body and the body surface contour point cloud data. Then, the captured three sets of
point cloud data will be aligned, replaced, registered by introducing feature points and iterated closest
point. The registered of non-topological relations of point cloud data for surface reconstruction can
get high precision of 3D human body model. The method of scanning time is shorter, at a relatively
low cost to build the 3D portrait with high identification model.

Key words: 3D human body model reconstruction, high identification degree of 3D human body
model, Kinect, iterated closest point, feature points