Blurred palmprint recognition based on DCT and block energy of principal lines

In view of the problem of blurred image caused by defocus status for non-contact palmprint collection,a novel recognition approach is proposed.As the stable features,the low frequency coefficients are extracted by discrete cosine transform(DCT) in the frequency domain,and the principal lines are extracted by the improved local gray minimum method in the spatial domain.The block method is used for calculating principal lines energy to form the feature vectors,then the stable features in the frequency and spatial domains are fused,and finally the Euclidean distance between vectors is used for classification and identification.The experiments based on the SUT-D blurred palmprint database show that compared with no-fusion and other typical identification methods,the proposed algorithm can get recognition rate up to 96.057 8%,which means that it is an effective and superior approach to solve the problem of blurred palmprint recognition.