tailieunhanh - báo cáo hóa học:" A framework of multi-template ensemble for fingerprint verification"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A framework of multi-template ensemble for fingerprint verification | EURASIP Journal on Advances in Signal Processing SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. A framework of multi-template ensemble for fingerprint verification EURASIP Journal on Advances in Signal Processing 2012 2012 14 doi 1687-6180-2012-14 Yilong Yin ylyin@ Yanbin Ning ningyanbin009@ Chunxiao Ren alanren@ Li Liu lliu20@ ISSN 1687-6180 Article type Research Submission date 5 July 2011 Acceptance date 19 January 2012 Publication date 19 January 2012 Article URL http content 2012 1 14 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in EURASIP Journal on Advances in Signal Processing go to http authors instructions For information about other SpringerOpen publications go to http 2012 Yin etal. licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A framework of multitemplate ensemble for fingerprint verification Yilong Yin Yanbin Ning Chunxiao Ren and Li Liu School of Computer Science and Technology Shandong University Jinan 250101 China Corresponding author ylyin@ Email addresses YY ylyin@ YN ningyanbin009 @163 .com CR alanren@ LL liu20@ Abstract How to improve performance of an automatic fingerprint verification system AFVS is always a big challenge in biometric verification field. Recently it becomes popular to improve the performance of AFVS using ensemble learning approach to fuse related .