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020 ▼a 9780438208773
035 ▼a (MiAaPQ)AAI10814964
035 ▼a (MiAaPQ)grad.msu:16066
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Ding, Yaohui.
24512 ▼a A Framework for Combining Ancillary Information with Primary Biometric Traits.
260 ▼a [S.l.] : ▼b Michigan State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 181 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Arun Ross.
5021 ▼a Thesis (Ph.D.)--Michigan State University, 2018.
520 ▼a Biometric systems recognize individuals based on their biological attributes such as faces, fingerprints and iris. However, in several scenarios, additional ancillary information such as the biographic and demographic information of a user (e.g.
520 ▼a The incorporation of ancillary information raises several challenges. Firstly, ancillary information such as gender, ethnicity and other demographic attributes lack distinctiveness and can be used to distinguish population groups rather than ind
520 ▼a In this regard, this dissertation makes three contributions. The first contribution entails the design of a Bayesian Belief Network (BBN) to model the relationship between biometric scores and ancillary factors, and exploiting the ensuing struct
520 ▼a In summary, this dissertation seeks to advance our understanding of systematically exploiting ancillary information in designing effective biometric recognition systems by developing and evaluating multiple statistical models.
590 ▼a School code: 0128.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Michigan State University. ▼b Computer Science - Doctor of Philosophy.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0128
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998148 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자