Normalized radial basis function networks and bilinear discriminant analysis for face recognition

In this paper, we present a novel approach for face recognition, using a new subspace method called bilinear discriminant analysis (BDA) and normalized radial basis function networks (NRBFNs). In a first step, BDA extracts the features that enhance separation between classes by using a generalized bilinear projection-based Fisher criterion, computed from image matrices directly. In a second step, the features are fed into a NRBFN that learns class conditional probabilities. This results in an efficient and computationally simple open-world identification process. Experimental results assess the performance and robustness of the proposed algorithm compared to other subspace methods combined with NRBFNs, in the presence of variations in head poses, facial expressions, and partial occlusions.