Abstract: In current years there has been an increasing interest in the study of sparse representation of signals. Using an over complete glossary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Recognition of persons by means of biometric description is an important technology in the society, because biometric identifiers cannot be shared and they intrinsically characterize the individualís bodily distinctiveness. Among several biometric recognition technologies, fingerprint compression is very popular for personal identification. One more fingerprint compression algorithm based on sparse representation using K-SVD algorithm is introduced. In the algorithm, First we construct a dictionary for predefined fingerprint photocopy patches. For a new given fingerprint images, suggest its patches according to the dictionary by computing l^0-minimization by MP method and then quantize and encode the representation.This paper compares dissimilar compression standards like JPEG,JPEG-2000,WSQ,K-SVDetc. The paper show that this is effective compared with several competing compression techniques particularly at high compression ratios.

Keywords: Compression, sparse representation, JPEG, JPEG 2000, WSQ, K-SVD.