Fingerprint Compression Based on Sparse Representation

被引：28

作者：

Shao, Guangqi ^{[1
]}

Wu, Yanping ^{[2
]}

Yong, A. ^{[1
]}

Liu, Xiao ^{[1
]}

Guo, Tiande ^{[1
]}

机构：

[1] Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China

[2] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2014年 / 23卷 / 02期

基金：

美国国家科学基金会;

关键词：

Fingerprint; compression; sparse representation; JPEG; 2000; WSQ; PSNR; OVERCOMPLETE DICTIONARIES; SIGNAL RECOVERY; K-SVD; IMAGE; ALGORITHM; MODEL;

D O I：

10.1109/TIP.2013.2287996

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining an overcomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For a new given fingerprint images, represent its patches according to the dictionary by computing l(0)-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficient compared with several competing compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.

引用

页码：489 / 501

页数：13

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