Radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition

This paper presents a pattern recognition framework for face recognition based on the combination of Radon and discrete cosine transforms (DCT). The property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. Data compaction property of DCT yields lower-dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. Further, DCT applied on Radon projections provides frequency features. The technique is invariant to in-plane rotation (tilt) and robust to zero mean white noise. The proposed algorithm is evaluated using FERET and ORL databases. The experimental results show the superiority of the proposed method compared to some of the existing algorithms.