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Human Face Identification Using 3D Neural Representation PDF Print E-mail
We study neural network approach for stereo image face recognition. Human Face Identification Using 3D Neural Representation, 3D Ident Person
We study neural network approach for stereo image face recognition. We consider the accepted leader in the field of 3D face recognition is A4Vision company (http://www.a4vision.com).
Their approach is in projecting structured light on the surface using infrared lattice (invisible to the human eye) , that is then taken by the videocamera in the infrared spectrum and according to the distortions of the lattice caused by the curvature of the face the model of the surface is built. After this is done the system measures different features of the face which are then considered to be a personal "code" and then introduced into the database and later used for matching.
The approach we study is different. Instead of structured light, we want to use common stereo vision for height map modelling as a continious function z = f(x,y) represented by a neural network. The particular face is thus represented in neural network coefficients. In this approach full face surface is stored in the database (not just face "code") in the compact form of neural network weights that allows more accurate surface mathing. In addition iterative neural network algorithm allows warps measurements, that is face comparison under different non-linear face distortions caused by mimics. The project is on the prototyping phase.
 
 
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