Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Fingerprint Indexing). Showing records 1 – 3 of 3 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of New South Wales

1. Zhou, Wei. Effective Solutions for Partial Fingerprint Indexing and Multi-Sensor Fingerprint Indexing.

Degree: Engineering & Information Technology, 2016, University of New South Wales

It is extremely challenging to identify a partial fingerprint against a large database due to the inability to narrow down the candidate list for partial fingerprint verification. Furthermore, the traditional capture of fingerprints based on the contact of the finger on a solid plane results in partial or degraded images. In this thesis, we aim to devise effective indexing schemes for partial fingerprint identification against very large scale databases. Furthermore, we have also acquired databases and developed identification techniques for the 3D images of fingerprints that have been generated by the new generation of touchless live scan devices. For partial fingerprint indexing, we propose to combine both local feature and global feature. We design some novel features of minutiae triplets in addition to some commonly used features to constitute the local minutiae triplet features. We then propose to combine a reconstructed global feature and local minutiae triplet features to improve the performance of partial fingerprint indexing. Specifically, the minutiae triplet based indexing scheme and the FOMFE coefficients based indexing scheme are applied separately to generate two candidate lists, then a fuzzy-based fusion scheme is designed to generate the final candidate list for matching.We have collected a multi-sensor fingerprint database to investigate the 3D fingerprint biometric comprehensively. It consists of 3D fingerprints as well as their corresponding 2D fingerprints captured by two commercial fingerprint scanners from 150 subjects in Australia. Also, we have tested the performance of 2D fingerprint verification, 3D fingerprint verification, and 2D to 3D fingerprint verification. In addition, the database has been released publicly for research purposes since 2015.For multi-sensor fingerprint indexing, we propose a finer hash bit selection method based on Locality-Sensitive Hashing (LSH) and Minutia Cylinder-code (MCC). That is, we divide the hash bit vectors, selected by LSH using a sliding window, into finer sub-vectors with certain fixed length, and then convert these sub-vectors into numerical approximations for MCC indexing. Also, we take into consideration another feature - the single maximum collision for indexing and fuse the candidate lists produced by both indexing methods to produce the final candidate list. Advisors/Committee Members: Hu, Jiankun, Engineering & Information Technology, UNSW Canberra, UNSW.

Subjects/Keywords: Multi-Sensor; Fingerprint Indexing; Partial

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhou, W. (2016). Effective Solutions for Partial Fingerprint Indexing and Multi-Sensor Fingerprint Indexing. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/57042 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42317/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Zhou, Wei. “Effective Solutions for Partial Fingerprint Indexing and Multi-Sensor Fingerprint Indexing.” 2016. Doctoral Dissertation, University of New South Wales. Accessed October 18, 2019. http://handle.unsw.edu.au/1959.4/57042 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42317/SOURCE01?view=true.

MLA Handbook (7th Edition):

Zhou, Wei. “Effective Solutions for Partial Fingerprint Indexing and Multi-Sensor Fingerprint Indexing.” 2016. Web. 18 Oct 2019.

Vancouver:

Zhou W. Effective Solutions for Partial Fingerprint Indexing and Multi-Sensor Fingerprint Indexing. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Oct 18]. Available from: http://handle.unsw.edu.au/1959.4/57042 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42317/SOURCE01?view=true.

Council of Science Editors:

Zhou W. Effective Solutions for Partial Fingerprint Indexing and Multi-Sensor Fingerprint Indexing. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/57042 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42317/SOURCE01?view=true

2. Jardini, Evandro de Araújo. MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico.

Degree: PhD, Processamento de Sinais de Instrumentação, 2007, University of São Paulo

O problema dos métodos tradicionais de identificação de pessoas é que são baseados em senhas e assim podem ser esquecidas, roubadas, perdidas, copiadas, armazenadas de maneira insegura e até utilizadas por uma pessoa que não tenha autorização. Os sistemas biométricos automáticos surgiram para oferecer uma alternativa para o reconhecimento de pessoas com maior segurança e eficiência. Uma das técnicas biométricas mais utilizadas é o reconhecimento de impressões digitais. Com o aumento do uso de impressões digitais nestes sistemas, houve o surgimento de grandes bancos de dados de impressões digitais, tornado-se um desafio encontrar a melhor e mais rápida maneira de recuperar informações. De acordo com os desafios apresentados, este trabalho tem duas propostas: i) desenvolver um novo algoritmo métrico para identificação de impressões digitais e ii) usá-lo para indexar um banco de dados de impressões digitais através de uma árvore de busca métrica. Para comprovar a eficiência do algoritmo desenvolvido foram realizados testes sobre duas bases de imagens de impressões digitais, disponibilizadas no evento Fingerprint Verification Competition dos anos de 2000 e 2002. Os resultados obtidos foram comparados com os resultados do algoritmo proposto por Bozorth. A avaliação dos resultados foi feita pela curva Receiver Operating Characteristic juntamente com a taxa de Equal Error Rate, sendo que, o método proposto, obteve a taxa de 4,9% contra 7,2% do método de Bozorth e de 2,0% contra 2,7% do Bozorth nos banco de dados dos anos de 2000 e 2002 respectivamente. Nos testes de robustez, o algoritmo proposto conseguiu identificar uma impressão digital com uma parte da imagem de apenas 30% do tamanho original e por se utilizar uma base de dados indexada, o mesmo obteve vantagens de tempo na recuperação de pequenas quantidades de impressões digitais de uma mesma classe.

The problem of the traditional methods of people identification is that they are based on passwords which may to be forgotten, stolen, lost, copied, stored in an insecure way and be used by unauthorized person. Automatic biometric systems appeared to provide an alternative for the recognition of people in a more safe and efficienty way. One most biometrics techniques used is the fingerprint recognition. With the increasing use of fingerprints in biometric systems, large fingerprint databases emerged, and with them, the challenge to find the best and fastest way to recover informations. According to the challenges previously mentioned, this work presents two proposals: i) to develop a newmetric algorithm for the identification of fingerprints and ii) to use it to index a fingerprint database using a metric search tree. To prove the efficiency of the developed algorithm tests were performed on two fingerprint images databases from Fingerprint Verification Competition of years 2000 and 2002. The obtained results were compared to the results of the algorithm proposed by Bozorth and was evaluated by the Receiver Operating Characteristic curve and the Equal Error Rate, where…

Advisors/Committee Members: Gonzaga, Adilson.

Subjects/Keywords: Biometria; Biometrics; Fingerprint indexing and metric space; Fingerprint recognition; Indexação de impressões digitais e espaço métrico; Reconhecimento de impressões digitais

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jardini, E. d. A. (2007). MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/18/18152/tde-04042008-143239/ ;

Chicago Manual of Style (16th Edition):

Jardini, Evandro de Araújo. “MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico.” 2007. Doctoral Dissertation, University of São Paulo. Accessed October 18, 2019. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-04042008-143239/ ;.

MLA Handbook (7th Edition):

Jardini, Evandro de Araújo. “MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico.” 2007. Web. 18 Oct 2019.

Vancouver:

Jardini EdA. MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico. [Internet] [Doctoral dissertation]. University of São Paulo; 2007. [cited 2019 Oct 18]. Available from: http://www.teses.usp.br/teses/disponiveis/18/18152/tde-04042008-143239/ ;.

Council of Science Editors:

Jardini EdA. MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico. [Doctoral Dissertation]. University of São Paulo; 2007. Available from: http://www.teses.usp.br/teses/disponiveis/18/18152/tde-04042008-143239/ ;


RMIT University

3. Wang, Y. Ridge orientation modeling and feature analysis for fingerprint identification.

Degree: 2008, RMIT University

This thesis systematically derives an innovative approach, called FOMFE, for fingerprint ridge orientation modeling based on 2D Fourier expansions, and explores possible applications of FOMFE to various aspects of a fingerprint identification system. Compared with existing proposals, FOMFE does not require prior knowledge of the landmark singular points (SP) at any stage of the modeling process. This salient feature makes it immune from false SP detections and robust in terms of modeling ridge topology patterns from different typological classes. The thesis provides the motivation of this work, thoroughly reviews the relevant literature, and carefully lays out the theoretical basis of the proposed modeling approach. This is followed by a detailed exposition of how FOMFE can benefit fingerprint feature analysis including ridge orientation estimation, singularity analysis, global feature characterization for a wide variety of fingerprint categories, and partial fingerprint identification. The proposed methods are based on the insightful use of theory from areas such as Fourier analysis of nonlinear dynamic systems, analytical operators from differential calculus in vector fields, and fluid dynamics. The thesis has conducted extensive experimental evaluation of the proposed methods on benchmark data sets, and drawn conclusions about strengths and limitations of these new techniques in comparison with state-of-the-art approaches. FOMFE and the resulting model-based methods can significantly improve the computational efficiency and reliability of fingerprint identification systems, which is important for indexing and matching fingerprints at a large scale.

Subjects/Keywords: Fields of Research; fingerprint ridge orientation modeling; model-based approach; Fourier series expansion; singularity analysis; fingerprint classification and indexing; partial fingerprint identification

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, Y. (2008). Ridge orientation modeling and feature analysis for fingerprint identification. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:6846

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Y. “Ridge orientation modeling and feature analysis for fingerprint identification.” 2008. Thesis, RMIT University. Accessed October 18, 2019. http://researchbank.rmit.edu.au/view/rmit:6846.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Y. “Ridge orientation modeling and feature analysis for fingerprint identification.” 2008. Web. 18 Oct 2019.

Vancouver:

Wang Y. Ridge orientation modeling and feature analysis for fingerprint identification. [Internet] [Thesis]. RMIT University; 2008. [cited 2019 Oct 18]. Available from: http://researchbank.rmit.edu.au/view/rmit:6846.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Ridge orientation modeling and feature analysis for fingerprint identification. [Thesis]. RMIT University; 2008. Available from: http://researchbank.rmit.edu.au/view/rmit:6846

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.