A novel face recognition method: Using random weight networks and quasi-singular value decomposition
This paper designs a novel human face recognition method, which is mainly based on a new feature extraction method and an efficient classifier  random weight network (RWN). Its innovation of the feature extraction is embodied in the good fusion of the geometric features and algebraic features of the original image. Here the geometric features are acquired by means of fast discrete curvelet transform (FDCT) and 2-dimensional principal component analysis (2DPCA), while the algebraic features are extracted by a proposed quasi-singular value decomposition (Q-SVD) method that can embody the relations of each image under a unified framework. Subsequently, the efficient RWN is applied to classify these fused features to further improve the recognition rate and the recognition speed. Some comparison experiments are carried out on six famous face databases between our proposed method and some other state-of-the-art methods. The experimental results show that the proposed method has an outstanding superiority in the aspects of separability, recognition rate and training time.
face recognition random weight networks quasi-singular value decomposition feature extraction