Glasses-faces Recognition Based on 2D Unsupervised Geodesic Discriminant Projection

A novel glasses-faces recognition method based on the 2D unsupervised geodesic discriminant projection (2DUGDP) technique is presented in this paper.Based on the virtual samples,discriminable features will be obtained by analyzing the difference of faces with variety eyeglasses.This feature characterizes the local scatter as well as the nonlocal scatter,seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter.The projection ensures the distance of samples remain close for near samples,and separate for far samples.Face space is regarded as a nonlinear instructure embedded in the high dimensional space.Geodesic distance is employed to model the intrinsic structure of the manifold.The method is applied to glasses-faces recognition and examination using the CAS-PEAL,FERET face databases.Results show that 2DUDP outperforms other methods.