Rotation, illumination invariant polynomial kernel Fisher discriminant analysis using Radon and discrete cosine transforms based features for face recognition

This paper presents an in-plane rotation (tilt), illumination invariant pattern recognition framework based on the combination of the features extracted using Radon and discrete cosine transforms and kernel based learning for face recognition. The use of Radon transform enhances the low frequency components, which are useful for face recognition and that of DCT yields low dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. DCT applied on Radon projections provides frequency features. Further, polynomial kernel Fisher discriminant analysis implemented on these features enhances discrimination capability of these features. The technique is also robust to zero mean white noise. The feasibility of the proposed technique has been evaluated using FERET, ORL, and Yale databases.