Face recognition based on ridgelet transforms

This paper relates to a face recognition and verification technique based on ridgelet transforms. Our proposed method first uses the ridgelet transform of the face image for feature extraction. This involves first applying a sequen- tial combination of radon and wavelet transforms to both the training and test images. The result is then decomposed into a set of feature vectors. The Euclidean distance between training and test feature vectors is finally used for the actual recognition. Before applying any transform on the training image, we normalize the image using a segmentation process based on the YCbCr colour model. This essentially detects the largest region of skin in the image. Experimental results using Yale, AT&T, faces94, faces96 and Indian databases show the superiority of the proposed method with some of the existing popular algorithms.